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Advances in Bias-aware
Recommendation on the Web
ACM WSDM 2021
14th ACM International Conference on Web Search and Data Mi...
About us
2
Ludovico Boratto
Senior Research Scientist
EURECAT - Centre Tecnológic de Catalunya
Barcelona, Spain
ludovicobo...
Learning objectives
● Raise awareness on the importance and the relevance of considering data and algorithmic bias issues
...
Outline and scheduling
● 08:30-10:00 Session I Foundations
○ 08:30 - 08:40 Welcome and Opening Remarks
○ 08:40 - 08:50 Sli...
SESSION I
Foundations
Part I.I
Recommendation
Principles
What products could I buy?
7
Advances in Bias-aware Recommendation on the Web
Boratto and Marras
Recommendation principles
What courses could I attend?
8
Advances in Bias-aware Recommendation on the Web
Boratto and Marras
Recommendation principl...
The problem
9
Advances in Bias-aware Recommendation on the Web
Boratto and Marras
Recommendation principles
Recommender
System
A solution
10
Advances in Bias-aware Recommendation on the Web
Boratto and Marras
Recommendation princi...
Capitalizing on recommender systems
A recommender system suggests items that might be relevant for a user
11
Advances in B...
The recommendation ranking task
12
● Given:
○ a set of consumers C = {c1
, c2
, ..., cM
}
○ a set of items I = {i1
, i2
, ...
Modes of optimization
13
● Pointwise optimization
point-wise approaches take a user-item pair and predict how relevant the...
Core recommendation techniques
Adapted from [Ricci et al. 2015]
14
Technique Background Input Process
Collaborative Rating...
Core stakeholders in recommendation
[Abdollahpouri et al. 2020]
A recommendation stakeholder is any group or individual th...
A sample multi-sided scenario
16
Consumers
Students
Providers
Teachers
System
Online Course Platform
Advances in Bias-awar...
Multi-sided recommendation aspects
[Abdollahpouri et al. 2020]
17
Aspect Definition
Multi-stakeholder design A multistakeho...
Part I.II
Hands on
Recommender Systems
Disclaimers
19
Advances in Bias-aware Recommendation on the Web
Boratto and Marras
● In this tutorial, we do not aim to sh...
Steps of this hands on
20
1
Data Load
We load data from publicly
available datasets, specifically
focusing on Movielens 1M
...
Part I.III
Data and Algorithmic Bias
Fundamentals
Motivating example in music
[Mehrotra et al. 2018]
● People frequently listen to music online
● Ratings and frequencies of...
Motivating example in education
[Boratto et al. 2019]
● Online course platforms are receiving great attention
● Student's ...
Motivating example in social platforms
[Edizel et al. 2020]
● Reading users' stories is a common activity nowadays
● Users...
Motivating example in recruiting
[Singh et al. 2018]
25
● Recruiters rely more and more on automated systems
● Based on th...
Tell your story
26
Tell us in a sentence an example experience where
you have dealt with biased behavior or outcomes
Disclaimers
27
Advances in Bias-aware Recommendation on the Web
Boratto and Marras
● We aim to focus on scientific literatu...
Related scientific venues and initiatives
28
Advances in Bias-aware Recommendation on the Web
Boratto and Marras
Scientific ...
Perspectives impacted by bias
29
Advances in Bias-aware Recommendation on the Web
Boratto and Marras
Economics
Law
Rights
...
How biases can influence economy
[Liu et al. 2019b]
● Loan recommender systems are designed to assist
lenders in looking fo...
Biases that impact on law
[Tolan et al. 2019]
The right to non-discrimination, which can be undermined by inherent biases,...
Biases that impact on rights
[Yao et al. 2017]
As an example, United Nations Sustainable Development Goal 4 aims also to e...
● Sellers might attack the system by introducing a bias in the ratings
● The attack goal is to bribe users to increase rat...
Biases associated to social dynamics
[Fabbri et al. 2020]
34
Advances in Bias-aware Recommendation on the Web
Boratto and ...
Ethical aspects influenced by bias
[Bozdag 2013, Milano et al. 2020]
35
Advances in Bias-aware Recommendation on the Web
Bo...
On biases against immoral content
[Pantakar et al. 2019]
36
Advances in Bias-aware Recommendation on the Web
Boratto and M...
Bias affecting users' privacy
[Resheff et al. 2018]
37
Advances in Bias-aware Recommendation on the Web
Boratto and Marras...
Influence of bias on autonomy
[Arnold et al. 2018]
38
Advances in Bias-aware Recommendation on the Web
Boratto and Marras
●...
How biases can introduce opacity
[Eslami et al. 2020]
39
Advances in Bias-aware Recommendation on the Web
Boratto and Marr...
Biases that emphasize unfairness
[Shakespeare et al. 2020]
40
Advances in Bias-aware Recommendation on the Web
Boratto and...
Biased effects on social exposure
[Papakyriakopoulos et al. 2020]
41
Advances in Bias-aware Recommendation on the Web
Bora...
Question time
42
In March 2016, Microsoft released a Twitter chatbot named “Tay” that was described
as an experiment in “c...
System objectives influenced by bias
[Kaminskas et al. 2017, Namatzadeh et al 2018, Singh et al. 2018]
43
Advances in Bias-...
Impact on utility (and trade-offs)
[Fu et al. 2020]
44
Advances in Bias-aware Recommendation on the Web
Boratto and Marras...
Impact on coverage (and trade-offs)
[Dean et al. 2020]
45
Advances in Bias-aware Recommendation on the Web
Boratto and Mar...
Impact on diversity (and trade-offs)
[Lee and Hosanagar 2019]
● They investigated the impact of collaborative
recommender ...
Impact on novelty (and trade-offs)
[Boratto et al. 2021]
47
Advances in Bias-aware Recommendation on the Web
Boratto and M...
Impact on providers' exposure
[Boratto et al. 2020]
48
Advances in Bias-aware Recommendation on the Web
Boratto and Marras...
SESSION II
Bias Mitigation
Part II.I
Bias through the
experimental pipeline
Data Acquisition
and Storage
Recommendation pipeline
51
Platform
Data
Model
Recommendations
Data
Preparation
Model
Predict...
Recommendation pipeline: users
52
Advances in Bias-aware Recommendation on the Web
Boratto and Marras
Data and algorithmic...
Types of bias associated to users
[Olteanu et al. 2017]
● Population biases
differences in demographics between a populati...
Bias on items due to their popularity
[Jannach et al. 2014]
● Context: movies, books, hotels, and mobile games
● Algorithm...
On behavioral bias towards popularity
[Cañamares and Castells 2018]
● Context: movies
● Algorithms: User KNN, Item KNN, Av...
Behavioral patterns on item categories
[Guo and Dunson 2015, Lin et al. 2019]
● Items of different genres have different r...
Biases conveyed by users' content
[Piramuthu et al. 2012, Xu et al. 2018, Dai et al. 2018, Vall et al. 2019]
● Sequential ...
Bias related to behavior over time
[Anelli et al. 2019]
58
Advances in Bias-aware Recommendation on the Web
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Recommendation pipeline: platform
59
Platform
Advances in Bias-aware Recommendation on the Web
Boratto and Marras
Data and...
Types of bias in platforms
[Olteanu et al. 2017]
● Functional biases
biases resulting from platform-specific mechanisms or ...
Functional bias in feedback loop
[Hofmann et al. 2014]
● Context: how a recommender systems evaluation
based on implicit f...
● Context: characterize the impact of human-system feedback loop in the context of recommender
systems, demonstrating the ...
Functional aspects influence choices
[Adomavicius et al 2013]
● Context: explore how consumer preferences are impacted by p...
Missing data biases internal functions
[Pradel et al. 2012]
● Context: study two major biases of the selection of
items, i...
Deceptive expectations bias user views
[Elsweiler et al. 2017]
● Context: they explore the feasibility of substituting
mea...
Example of external bias
[Jahanbakhsh et al. 2020]
● Context: study how the ratings people receive on online labor
platfor...
Bias introduced by external viewpoints
[Schwind et al. 2012]
● Context: when a diversity of viewpoints is available, learn...
On decision biases in user's choices
[Teppan and Zanker 2015]
● Context: experimental analysis of the impact of different ...
Question time
69
How can bias be emphasized in the recommendation pipeline? Please,
provide an example source of bias for ...
Data Acquisition
and Storage
Recommendation pipeline: data
70
Data
Data
Preparation
Advances in Bias-aware Recommendation ...
Sources of bias in data collection
[Olteanu et al. 2017]
● Data acquisition
○ discouraging data collection by third partie...
Sources of bias in data preparation
[Olteanu et al. 2017]
● Data cleaning
○ data representation choices and default values...
Recommendation pipeline: model
73
Model
Recommendations
Model
Prediction
Recommendation
Delivering
Model
Evaluation
Advanc...
Sources of bias in model exploitation
[Olteanu et al. 2017]
● Qualitative analysis
○ data representation choices and defau...
Sources of bias in model evaluation
[Olteanu et al. 2017, Bellogín et al. 2017]
● Evaluation data selection
○ imbalances o...
Bias on popularity in evaluation
[Bellogín et al. 2017]
76
Advances in Bias-aware Recommendation on the Web
Boratto and Ma...
Bias and random decoys in evaluation
[Ekstrand et al. 2017] [Other readings: Lim et al. 2015, Yang et al. 2018, Carraro et...
Bias on error estimation in evaluation
[Tian et al. 2020]
● Context: offline evaluation cannot accurately
assess novel, rel...
Bias-aware process pipeline
79
IDENTIFY PRODUCT GOALS
● What are you trying to achieve?
● For what population of people?
●...
Techniques for bias treatment
80
Pre-processing
before model training
In-processing
during model training
Post-processing
...
Example of pre-processing treatment
[Jannach et al. 2015]
● Idea: extending BPR with a modified distribution
function φ tha...
Example of in-processing treatment
[Abdollahpouri et al. 2017]
● Idea: identify a regularization component of the
objectiv...
Example of post-processing treatment
[Abdollahpouri et al. 2019]
● Idea: they modified the xQuAD to produce a new re-ranked...
Other treatments for popularity bias
among others...
● Treatments that manipulate interactions before training a model:
○ ...
Treatments against other biases (1)
● Biases related to how items are sampled, positioned, and/or selected, e.g.:
○ connec...
Treatments against other biases (2)
● Biases associated to how items reach their audience:
○ a novel probabilistic method ...
Treatments against other biases (3)
● Biases associated to reviews and textual opinions, e.g.:
○ a sentiment classification...
Treatments against other biases (4)
● Biases associated to social trust and influence:
○ a mitigation using polynomial regr...
Treatments against other biases (5)
● Biases related to the interactions of users over time, e.g.:
○ an historical influenc...
Part II.II
Hands on
Item Popularity Bias
Steps of this hands on
91
1
Model Exploration
We consider on data and
models introduced in the first
hands on to inspect of...
Session III
Unfairness Mitigation
Part III.I
Discrimination
Biases that lead to discrimination
[Mehrabi et al. 2019]
94
Advances in Bias-aware Recommendation on the Web
Boratto and M...
Granularity of discrimination
[Mehrabi et al. 2019]
95
Advances in Bias-aware Recommendation on the Web
Boratto and Marras...
Contextualizing to recommendation
● Individual and sub-group discrimination are very challenging to assess: user similarit...
Types of disparity in groups
[Mehrabi et al. 2019]
97
Advances in Bias-aware Recommendation on the Web
Boratto and Marras
...
Contextualizing disparities
● Recommender systems usually do not receive as input any sensitive attribute of the user, so ...
Definitions of fairness
[Mehrabi et al. 2019]
99
Advances in Bias-aware Recommendation on the Web
Boratto and Marras
Equali...
Fairness metrics for ranked outputs (I)
[Yang and Stoyanovich 2017, Farnadi et al. 2018, Sonboli et al. 2019, Deldjoo et a...
Fairness metrics for ranked outputs (II)
[Yang and Stoyanovich 2017, Farnadi et al. 2018, Sonboli et al. 2019, Deldjoo et ...
Multi-sided fairness
[Burke et al. 2017]
102
Advances in Bias-aware Recommendation on the Web
Boratto and Marras
Consumer-...
Impact of bias on consumer fairness (I)
[Ekstrand et al. 2018a]
● Context: they investigate whether demographic groups
obt...
● Context: investigate three user groups from Last.fm based on how much their listening preferences
deviate from the most ...
Impact of bias on provider fairness
[Ekstrand et al. 2018b]
● Context: examine the response of collaborative filtering algo...
Have your say
106
Tell us in a sentence up to 3 examples of domains or
tasks you work on and how they showed unfairness
Example of pre-processing treatment
[Rastegarpanah et al. 2019]
● Idea: augmenting the input with additional data can
impr...
Example of in-processing treatment
[Beutel et al. 2019]
● Idea: propose a metric for measuring fairness based on
pairwise ...
Example of post-processing treatment
[Liu et al. 2019b]
● Idea: combining of a personalization-induced term and
a fairness...
Other treatments against C-fairness
among others...
● Strategies that introduce regularization or constraints during train...
Treatments for C-fairness of groups
among others...
● Stratigi et al. 2017:
○ improving the opportunities that patients ha...
Other treatments against P-fairness
among others...
● Strategies involving some degree of pre-processing:
○ a mitigation a...
Treatments with a multi-sided focus
among others...
● In a speed-dating domain:
○ in a speed-dating context, a multi-dimen...
Part III.II
Hands on
Item Provider Fairness
Steps of this hands on
115
1
Model Exploration
We consider on data and
models introduced in the first
hands on to inspect o...
Part IV
Concluding Remarks
Contextual challenges
117
Advances in Bias-aware Recommendation on the Web
Boratto and Marras
● Stakeholders have differen...
Operational challenges
118
Advances in Bias-aware Recommendation on the Web
Boratto and Marras
● Measuring and operational...
Evaluation challenges
● What if we do not have the sensitive attributes in the collected data?
● How should we select an a...
Resources from this tutorial
1. Tutorial website https://biasinrecsys.github.io/wsdm2021/
2. Github repository https://git...
Your feedback is
essential for us!
https://forms.gle/HaxAMNwAWxSacDgD9
Questions?
References #1
1. Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, J...
References #2
9. Alejandro Bellogín, Pablo Castells, Iván Cantador: Statistical biases in Information Retrieval metrics fo...
References #3
20. Rocío Cañamares, Pablo Castells: Should I Follow the Crowd?: A Probabilistic Analysis of the Effectivene...
References #4
29. Jiao Dai, Mingming Li, Songlin Hu, Jizhong Han: A Hybrid Model Based on the Rating Bias and Textual Bias...
References #5
38. Michael D. Ekstrand, Mucun Tian, Mohammed R. Imran Kazi, Hoda Mehrpouyan, Daniel Kluver: Exploring autho...
References #6
48. Farnaz Jahanbakhsh, Justin Cranshaw, Scott Counts, Walter S. Lasecki, Kori Inkpen: An Experimental Study...
References #7
57. Dokyun Lee, Kartik Hosanagar: How Do Recommender Systems Affect Sales Diversity? A Cross-Category Invest...
References #8
68. Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, Aram Galstyan. A survey on bias and ...
References #9
78. Gourab K. Patro, Arpita Biswas, Niloy Ganguly, Krishna P. Gummadi, Abhijnan Chakraborty: FairRec: Two-Si...
References #10
87. Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, Kazuhide Nakata: Unbiased Recommender Learnin...
References #11
99. Songül Tolan: Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges. CoRR ...
References #12
110. Ke Yang, Julia Stoyanovich: Measuring Fairness in Ranked Outputs. SSDBM 2017: 22:1-22:6 (2017).
111. S...
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Tutorial on Advances in Bias-aware Recommendation on the Web @ WSDM 2021

  1. 1. Advances in Bias-aware Recommendation on the Web ACM WSDM 2021 14th ACM International Conference on Web Search and Data Mining March 8, 2021 08:30 13:00 – ONLINE from Jerusalem
  2. 2. About us 2 Ludovico Boratto Senior Research Scientist EURECAT - Centre Tecnológic de Catalunya Barcelona, Spain ludovicoboratto.com ludovico.boratto@acm.org Mirko Marras Postdoctoral Researcher École Polytechnique Fédérale de Lausanne (EPFL) Lausanne, Switzerland mirkomarras.com mirko.marras@acm.org Advances in Bias-aware Recommendation on the Web Boratto and Marras
  3. 3. Learning objectives ● Raise awareness on the importance and the relevance of considering data and algorithmic bias issues in recommendation ● Play with recommendation pipelines and conduct exploratory analysis aimed at uncovering sources of bias along them ● Showcase approaches that mitigate bias along with the recommendation pipeline and assess their influence on stakeholders ● Provide an overview on the current trends and challenges in bias-aware research and identify new research directions 3 Advances in Bias-aware Recommendation on the Web Boratto and Marras
  4. 4. Outline and scheduling ● 08:30-10:00 Session I Foundations ○ 08:30 - 08:40 Welcome and Opening Remarks ○ 08:40 - 08:50 Slides Recommendation Principles ○ 08:50 - 09:10 Notebook Hands on Recommender Systems ○ 09:10 - 09:50 Slides Data and Algorithmic Bias Fundamentals ● 09:50-10:00 Coffee Break ● 10:00-11:20 Session II Bias Mitigation ○ 10:00 - 10:45 Slides Mitigation Techniques for Bias ○ 10:45 - 11:20 Notebook Hands on Item Popularity Bias ● 11:20-11:30 Coffee Break ● 11:30-13:00 Session III Unfairness Mitigation ○ 11:30 - 12:10 Slides Unfairness and Mitigation Strategies ○ 12:10 - 12:45 Notebook Hands on Item Provider Unfairness ○ 12:45 - 13:00 Concluding Remarks All times are displayed in conference local time (GMT +2) 4 Advances in Bias-aware Recommendation on the Web Boratto and Marras
  5. 5. SESSION I Foundations
  6. 6. Part I.I Recommendation Principles
  7. 7. What products could I buy? 7 Advances in Bias-aware Recommendation on the Web Boratto and Marras Recommendation principles
  8. 8. What courses could I attend? 8 Advances in Bias-aware Recommendation on the Web Boratto and Marras Recommendation principles
  9. 9. The problem 9 Advances in Bias-aware Recommendation on the Web Boratto and Marras Recommendation principles
  10. 10. Recommender System A solution 10 Advances in Bias-aware Recommendation on the Web Boratto and Marras Recommendation principles
  11. 11. Capitalizing on recommender systems A recommender system suggests items that might be relevant for a user 11 Advances in Bias-aware Recommendation on the Web Boratto and Marras Recommendation principles
  12. 12. The recommendation ranking task 12 ● Given: ○ a set of consumers C = {c1 , c2 , ..., cM } ○ a set of items I = {i1 , i2 , ..., iN } ● Let R ⊆ R M ×N be the consumer-item feedback matrix: ○ R(c,i) ≥ 0 if consumer c expressed interest in item i ○ R(c,i) = 0 otherwise ● The objective is to predict unobserved consumer-item feedback R(c,i) = f(c,i | θ) in R: ○ θ denotes model parameters ○ f denotes the function that maps model parameters to the predicted relevance ● Given a consumer c, items not rated by c are ranked by decreasing relevance: i* = arg max f(c,j | θ) j ∈ I Ic Advances in Bias-aware Recommendation on the Web Boratto and Marras Recommendation principles
  13. 13. Modes of optimization 13 ● Pointwise optimization point-wise approaches take a user-item pair and predict how relevant the item is for that user ● Pairwise optimization pair-wise approaches digest a triplet of user, observed item, and unobserved item, and minimize the cases when the unobserved item is more relevant than the observed item for that user ● Listwise optimization list-wise approaches look at the entire list and build the optimal ordering for that user Advances in Bias-aware Recommendation on the Web Boratto and Marras Recommendation principles
  14. 14. Core recommendation techniques Adapted from [Ricci et al. 2015] 14 Technique Background Input Process Collaborative Ratings from C of items in I Ratings from c of items in I Identify users in C similar to c and extrapolate from their preferences of i Content-based Features of items in I Ratings from c of items in I Generate a classifier that fits c's rating behavior and use it on i Demographic Demographic information on C and their ratings of items in I Demographic information on c Identify users that are demographically similar to c and extrapolate from their preferences of i Utility-based Features of items in I A utility function over items in I that describes c's preferences Apply the function to the items and determine i's rank Knowledge-based Features of items in I and knowledge of how these items meet a user's need A description of c's needs or interests Infer a match between i and c's needs or interests Advances in Bias-aware Recommendation on the Web Boratto and Marras Recommendation principles
  15. 15. Core stakeholders in recommendation [Abdollahpouri et al. 2020] A recommendation stakeholder is any group or individual that can affect, or is affected by, the delivery of recommendations to users 15 Consumers Providers System C P S Advances in Bias-aware Recommendation on the Web Boratto and Marras Recommendation principles
  16. 16. A sample multi-sided scenario 16 Consumers Students Providers Teachers System Online Course Platform Advances in Bias-aware Recommendation on the Web Boratto and Marras Recommendation principles
  17. 17. Multi-sided recommendation aspects [Abdollahpouri et al. 2020] 17 Aspect Definition Multi-stakeholder design A multistakeholder design process is one in which different recommendation stakeholder groups are identified and consulted in the process of system design Multi-stakeholder algorithm A multistakeholder recommendation algorithm takes into account the preferences of multiple parties when generating recommendations, especially when these parties are on different sides of the recommendation interaction Multi-stakeholder evaluation A multistakeholder evaluation is one in which the quality of recommendations is assessed across multiple groups of stakeholders, in addition to a point estimate over the full user population Advances in Bias-aware Recommendation on the Web Boratto and Marras Recommendation principles
  18. 18. Part I.II Hands on Recommender Systems
  19. 19. Disclaimers 19 Advances in Bias-aware Recommendation on the Web Boratto and Marras ● In this tutorial, we do not aim to show how to fine-tune algorithms ● Due to the time constraints, we decided to reduce the optimization part ● The pre-trained models do not represent fine-tuned baselines ● The goal is to get familiar with an environment where it is easier to control the whole recsys process Hands on Recommender Systems
  20. 20. Steps of this hands on 20 1 Data Load We load data from publicly available datasets, specifically focusing on Movielens 1M (movies) Data Pre-Processing We process data to be fed into the model and we prepare training based on point- and pair-wise methods 2 Model Definition and Train We define the architecture of the model, setup the training parameters and run the model training process 3 Relevance Computation Given a pre-trained model, we compute the user-item relevance scores across all the user-item pairs 4 Model Evaluation We compute accuracy and beyond-accuracy metrics, such as coverage, novelty, and diversity 5 Advances in Bias-aware Recommendation on the Web Boratto and Marras https://colab.research.google.com/github/biasinrecsys/wsdm2021/blob/master/notebooks/model_setup.ipynb Hands on Recommender Systems
  21. 21. Part I.III Data and Algorithmic Bias Fundamentals
  22. 22. Motivating example in music [Mehrotra et al. 2018] ● People frequently listen to music online ● Ratings and frequencies often used to learn patterns ● 1/3 of users listen to at least 20% of unpopular artists ● Why are popular artists favoured? ● Why do users who tend to interact with niche artists receive the worst recommendations? 22 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals > Motivating examples
  23. 23. Motivating example in education [Boratto et al. 2019] ● Online course platforms are receiving great attention ● Student's preferences learnt from ratings/enrolments ● The imbalance in popularity among courses reinforces coverage and concentration biases of ranked courses ● Popularity bias could impede new courses to emerge ● The market could be dominated by a few teachers 23 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals > Motivating examples
  24. 24. Motivating example in social platforms [Edizel et al. 2020] ● Reading users' stories is a common activity nowadays ● Users can vote stories up and down ● Gender attributes are not supported by Reddit ● Why recommender systems reinforce the imbalance between genders while suggesting reddits? ● 95% (87%) of subreddits popular among females (males) show imbalance reinforcement over genders 24 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals > Motivating examples
  25. 25. Motivating example in recruiting [Singh et al. 2018] 25 ● Recruiters rely more and more on automated systems ● Based on the job, "best" candidates are suggested ● Small differences in relevance can lead to large differences in exposure among candidate groups ● Is this winner-take-all allocation of exposure fair, even if the winner just has a tiny advantage in relevance? ● It might be fairer to distribute exposure proportional to relevance, even if this leads to a drop in utility Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals > Motivating examples
  26. 26. Tell your story 26 Tell us in a sentence an example experience where you have dealt with biased behavior or outcomes
  27. 27. Disclaimers 27 Advances in Bias-aware Recommendation on the Web Boratto and Marras ● We aim to focus on scientific literature that specifically consider recommender systems ● Pointers to representative scientific events on related concepts applied to ranking systems are given ● References discussed throughout the slides would not be exhaustive ● Refer to the extended bibliography attached to this tutorial for a more comprehensive list of papers Data and algorithmic bias fundamentals > Motivating examples
  28. 28. Related scientific venues and initiatives 28 Advances in Bias-aware Recommendation on the Web Boratto and Marras Scientific tutorials including: Dedicated workshops or tracks including: Papers in top-tier conferences including: ● RecSys ● SIGIR ● The Web Conf ● TREC ● UMAP ● WSDM ● CIKM ● ECIR ● KDD ● FAccTRec @ RecSys 2018-2020 ● RMSE & Impact RS @ RecSys 2019 ● FACTS-IR @ SIGIR 2019 ● FATES @ The Web Conf 2019-21 ● FAIR-TREC @ TREC 2020-21 ● FairUMAP @ UMAP 2018-20 ● DAB @ CIKM 2017-19 ● Bias @ ECIR 2020-21 ● FAT-ML @ ICML 2014-2019 ● Fairness and Discrimination in Retrieval and Recommendation @ SIGIR 2019 & RecSys 2019 ● Learning to Rank in theory and practice: From Gradient Boosting to Neural Networks and Unbiased Learning @ SIGIR 2019 ● Multi-stakeholder Recommendations: Case Studies, Methods and Challenges @ RecSys 2019 ● Experimentation with fairness-aware recommendation using librec-auto @ FAT 2020 ● Hands-on on Data and Algorithmic Bias in Recommender Systems @ UMAP 2020 ● Bias in Personalized Rankings: Concepts to Code @ ICDM 2020 Data and algorithmic bias fundamentals Special issues in journals including: ● Special Issue on Fair, Accountable, and Transparent Recommender Systems @ UMUAI ● Special Issue on Algorithmic Bias and Fairness in Search and Recommendation @ IPM
  29. 29. Perspectives impacted by bias 29 Advances in Bias-aware Recommendation on the Web Boratto and Marras Economics Law Rights Security Social Dynamics bias can introduce disparate impacts among providers, influencing future success and revenues bias can affect core user's rights that are regulated by law, such as fairness and discrimination bias can reinforce discrimination of certain user's groups, including ageism, sexism, homophobia bias can lead certain groups of users or an entire system to be more vulnerable to attacks (e.g., bribery) bias can influence how labour markets evolve and can be amplified as the technology progresses Data and algorithmic bias fundamentals > Shaping the context
  30. 30. How biases can influence economy [Liu et al. 2019b] ● Loan recommender systems are designed to assist lenders in looking for promising borrowers ● Such systems model lenders’ historical behaviors and generate personalized recommendations ● Certain geographical regions such as Asia and Africa dominate recommendation ● Others like Oceania and Eastern Europe barely receive recommendations 30 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals > Shaping the context
  31. 31. Biases that impact on law [Tolan et al. 2019] The right to non-discrimination, which can be undermined by inherent biases, is embedded in the normative framework of the European Union, e.g.: ● Mentions can be found in Art. 21 of the EU Charter of Fundamental Rights ● Article 14 of the European Convention on Human Rights ● Articles 18-25 of the Treaty on the Functioning of the European Union 31 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals > Shaping the context
  32. 32. Biases that impact on rights [Yao et al. 2017] As an example, United Nations Sustainable Development Goal 4 aims also to ensure equitable quality education for all, but Yao et al. observed that: ● In 2010, women accounted for only 18% of the BSc degrees in Computer Science ● Historical rating data of Computer Science courses to be dominated by men ● The model may underestimate women’s preferences and be biased towards men 32 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals > Shaping the context
  33. 33. ● Sellers might attack the system by introducing a bias in the ratings ● The attack goal is to bribe users to increase ratings and push recommendations ● A novel Hybrid KNN CF is introduced to deal with this phenomenon ● By means of this novel algorithm: ○ The profitability associated to increasing the ratings is strongly reduced w.r.t. SVD ○ Downgrading the ratings of competitors is not profitable with this approach ○ System is more robust to attacks and more trustable by the users Biases can affect security aspects [Ramos et al. 2020] 33 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals > Shaping the context
  34. 34. Biases associated to social dynamics [Fabbri et al. 2020] 34 Advances in Bias-aware Recommendation on the Web Boratto and Marras ● People recommendation in social networks, with users divided into groups based on gender ● A range of state-of-the-art algorithms, such as Adamic-Adar, SALSA, and ALS are inspected ● People recommenders produce disparate visibility on the two subgroups of gender ● Homophily plays a key role in promoting or reducing visibility for different subgroups Lorenz Curves (inequality). Recommendations introduce more inequality than degree distribution, and this inequality is stronger in the minority class. Data and algorithmic bias fundamentals > Shaping the context
  35. 35. Ethical aspects influenced by bias [Bozdag 2013, Milano et al. 2020] 35 Advances in Bias-aware Recommendation on the Web Boratto and Marras Moral recommendation of inappropriate content Opacity black-box algorithms, uninformative explanations, feedback effects Privacy unauthorised data collection, data leaks, unauthorised inferences Fairness observation bias, population imbalance Autonomy and Identity behavioural traps and encroachment on sense of personal autonomy Social Exposure lack of exposure to contrasting viewpoints, feedback effects Data and algorithmic bias fundamentals > Shaping the context
  36. 36. On biases against immoral content [Pantakar et al. 2019] 36 Advances in Bias-aware Recommendation on the Web Boratto and Marras ● News recommender system generates awareness on biased news, to possibly avoid fake and politically polarized news ● They propose a news clustering and bias score attached to each news. Recommendation of similar, unbiased content ● With a live-user evaluation, the rankings generated by the algorithm match with the ones the users would generate Data and algorithmic bias fundamentals > Shaping the context
  37. 37. Bias affecting users' privacy [Resheff et al. 2018] 37 Advances in Bias-aware Recommendation on the Web Boratto and Marras ● User representations may be used to recover private user information such as gender and age, undermining users' privacy ● A privacy-adversarial framework is proposed to eliminate leakage of private information: an adversarial component is appended to the model for each of the demographic variables we want to obfuscate, so that the user representations are optimized to preclude predicting the variables ● Privacy preserving recommendations, minimal overall adverse effect on recommender performance, fairness of results (all knowledge of the attributes is scrubbed from the representations used by the model) Data and algorithmic bias fundamentals > Shaping the context
  38. 38. Influence of bias on autonomy [Arnold et al. 2018] 38 Advances in Bias-aware Recommendation on the Web Boratto and Marras ● Text recommender systems that support creative tasks (writing reviews). Do they exhibit unintentional biases in the support that they offer? ● Contextual recommendations are proposed: (1) selection of the three most likely next words, (2) generation of the most likely phrase continuation for each word ● People who get recommended phrasal text entry shortcuts that are skewed positive, write more positive reviews than when presented with negative-skewed shortcuts Data and algorithmic bias fundamentals > Shaping the context
  39. 39. How biases can introduce opacity [Eslami et al. 2020] 39 Advances in Bias-aware Recommendation on the Web Boratto and Marras ● Opaque algorithms sometimes make biased or deceptive decisions, many have called for increased transparency ● They conducted an analysis of 242 users’ online discussions about the Yelp review filtering algorithm ● Users defend this algorithm and its opacity depending on their engagement with and gain from the algorithm ● Then, adding transparency into the algorithm changed users’ attitudes towards the algorithm Data and algorithmic bias fundamentals > Shaping the context (a) A filtered review is presented as “recommended” to the user who wrote it; (b) This review, however, presented for other users as a filtered review.
  40. 40. Biases that emphasize unfairness [Shakespeare et al. 2020] 40 Advances in Bias-aware Recommendation on the Web Boratto and Marras ● An exploratory study, analyzing the extent to which commonly deployed state-of-the-art collaborative filtering algorithms (CF) may act to further increase/decrease artist gender bias ● They show that gender bias can propagate in CF-based recommendations, according to the bias present in the data Data and algorithmic bias fundamentals > Shaping the context
  41. 41. Biased effects on social exposure [Papakyriakopoulos et al. 2020] 41 Advances in Bias-aware Recommendation on the Web Boratto and Marras ● A segment of the political discussions on social networks is shaped by users that are over-proportionally active ● By applying geometric topic modeling on German political comments, they demonstrate that hyperactive users have a significant role in the political discourse ● By training recommender systems, they illustrate that models provide very different suggestions to users, when accounting for or ignoring hyperactive behavior Data and algorithmic bias fundamentals > Shaping the context
  42. 42. Question time 42 In March 2016, Microsoft released a Twitter chatbot named “Tay” that was described as an experiment in “conversational understanding.” The bot was supposed to learn to engage with people through “casual and playful conversation”. But Twitter users engaged in conversation that was not so casual and playful. Within 24 hours, Tay was tweeting about racism, anti-semitism, dictators, and more. Part of it was prompted by users asking the bot to repeat after them, but soon the bot started saying strange and offensive things on its own. What ethical aspects have been undermined in that circumstance? A. Moral B. Privacy C. Fairness D. Autonomy and Identity E. Opacity F. Social Exposure Full story at https://www.cnbc.com/2018/03/17/facebook-and-youtube-should-learn-from-microsoft-tay-racist-chatbot.html#BoxInline-ArticleBody-5:~:text=Where%20M icrosoft%20went%20wrong
  43. 43. System objectives influenced by bias [Kaminskas et al. 2017, Namatzadeh et al 2018, Singh et al. 2018] 43 Advances in Bias-aware Recommendation on the Web Boratto and Marras Utility Recommendation Objectives Novelty Diversity Coverage Serendipity the degree to which recommended items are potentially useful and of interest for the user the degree of attention received by (groups of) items or providers the degree to which the list has valuable items not looked for and generate surprise for the user the degree to which the generated recommendations cover the catalog of available items the degree to which the list of retrieved items covers a broad area of the information space the degree to which items are unknown by the user and/or are different from what the user has seen before Visibility & Exposure Data and algorithmic bias fundamentals > Shaping the context
  44. 44. Impact on utility (and trade-offs) [Fu et al. 2020] 44 Advances in Bias-aware Recommendation on the Web Boratto and Marras ● Studying recommendation performance according to the level of activity of users: ○ Inactive users are more susceptible to unsatisfactory recommendations (insufficient training data) ○ Recommendations are biased by the training records of more active users ● They proposed an explainable CF + re-ranking to balance predictions and group/individual fairness ● The disparity in utility is reduced while preserving recommendation quality Data and algorithmic bias fundamentals > Shaping the context
  45. 45. Impact on coverage (and trade-offs) [Dean et al. 2020] 45 Advances in Bias-aware Recommendation on the Web Boratto and Marras ● The amount of recourse available to a user is the percentage of unseen items that are reachable ● The availability of items in a recommender system is the percentage of items reachable by a user ● Study on linear preference models (SLIM and MF) ● Unavailable items are less popular than available items ● Users with smaller history have more available recourse Data and algorithmic bias fundamentals > Shaping the context
  46. 46. Impact on diversity (and trade-offs) [Lee and Hosanagar 2019] ● They investigated the impact of collaborative recommender algorithms commonly used in e-commerce on sales diversity ● The use of traditional collaborative filters is associated with a decrease in sales diversity relative to a world without product recommendations ● The decrease in aggregate sales diversity may not always be accompanied by a corresponding decrease in individual-level consumption diversity 46 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals > Shaping the context
  47. 47. Impact on novelty (and trade-offs) [Boratto et al. 2021] 47 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals > Shaping the context ● They show that the probability of being recommended and the item true positive rate are biased against the item popularity ● They propose an in-processing approach aimed at minimizing the biased correlation between user-item relevance and item popularity ● With small losses in accuracy, their popularity-mitigation approach leads to important gains in beyond-accuracy recommendation quality, especially novelty
  48. 48. Impact on providers' exposure [Boratto et al. 2020] 48 Advances in Bias-aware Recommendation on the Web Boratto and Marras ● Certain minority groups of providers are being disproportionately affected by unintentional discrimination ● They show how adding observation upsampling can dramatically change the exposure given to providers ● The ranked lists then provide fairer exposure, wider minority-group item coverage, and limited loss in utility Data and algorithmic bias fundamentals > Shaping the context
  49. 49. SESSION II Bias Mitigation
  50. 50. Part II.I Bias through the experimental pipeline
  51. 51. Data Acquisition and Storage Recommendation pipeline 51 Platform Data Model Recommendations Data Preparation Model Prediction Recommendation Delivering Model Evaluation Advances in Bias-aware Recommendation on the Web Boratto and Marras Pre- Processed Data Model Setup and Training Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  52. 52. Recommendation pipeline: users 52 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  53. 53. Types of bias associated to users [Olteanu et al. 2017] ● Population biases differences in demographics between a population of users represented in a dataset/platform and a target population ● Behavioral biases differences in user behavior across platforms or contexts, or across users represented in different datasets ● Content biases behavioral biases that are expressed as lexical, syntactic, semantic, and structural differences in the contents generated by users ● Linking biases behavioral biases expressed as differences in the attributes of networks obtained from user connections, interactions or activity ● Temporal biases differences in populations or behaviors over time 53 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  54. 54. Bias on items due to their popularity [Jannach et al. 2014] ● Context: movies, books, hotels, and mobile games ● Algorithms: CB-Filtering, SlopeOne, User-KNN, Item-KNN, FM, RfRec, Funk-SVD, Koren-MF, ALS, BPR ● Findings: techniques performing well on accuracy focus their recommendations on a tiny fraction of the item spectrum or recommend mostly top sellers 54 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  55. 55. On behavioral bias towards popularity [Cañamares and Castells 2018] ● Context: movies ● Algorithms: User KNN, Item KNN, AvgRating, MF, Random, Pop ● Findings: effectiveness or ineffectiveness of popularity depends on the interplay of three main variables: item relevance, item discovery by users, and the decision by users to interact with discovered items. Authors identify the key probabilistic dependencies among these factors 55 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  56. 56. Behavioral patterns on item categories [Guo and Dunson 2015, Lin et al. 2019] ● Items of different genres have different rating values and different samples ● Bayesian multiplicative probit model to uncover category-wise bias in ratings 56 Advances in Bias-aware Recommendation on the Web Boratto and Marras ● User preferences propagate differently in recommendations, according to movie genre and user gender ● SVD++ and BiasedMF dampen the preference bias for movie genres for both men and women ● WRMF is well-calibrated for Sci-Fi/Crime for both men and women but the behavior is inconsistent for Action/Romance Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  57. 57. Biases conveyed by users' content [Piramuthu et al. 2012, Xu et al. 2018, Dai et al. 2018, Vall et al. 2019] ● Sequential bias: the sequence in which reviews are written play an appreciable role in how the next reviews are written ● Opinion bias: given a user–item pair, the opinion bias is defined as the bias between rating and review. The rating matrix is filled with a linear combination of the rating and the review sentiment ● Textual bias inspects how recommenders are influenced by the fact that words may express different meanings in review context 57 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  58. 58. Bias related to behavior over time [Anelli et al. 2019] 58 Advances in Bias-aware Recommendation on the Web Boratto and Marras ● Context: Popularity is a local concept and a function of time. Popular and recently rated items are deemed as relevant for the user. Movie and toy recommendation. ● Algorithms: User KNN with the concept of precursor neighbors and a time decay ● Findings: Time-aware neighbors and local popularity lead to a comparable effectiveness (in terms of NDCG w/ time-independent rating order condition) + an improved efficiency Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  59. 59. Recommendation pipeline: platform 59 Platform Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  60. 60. Types of bias in platforms [Olteanu et al. 2017] ● Functional biases biases resulting from platform-specific mechanisms or affordances, that is, the possible actions within each system or environment ● Normative biases biases that are a result of written norms or expectations about unwritten norms describing acceptable patterns of behavior on a given platform ● External biases biases resulting from factors outside the platform, including considerations of socioeconomic status, education, social pressure, privacy concerns, interests, language, personality, and culture ● Non-individual accounts interactions on social platforms that are not produced by individuals, but by accounts representing various types of organizations, or by automated agents 60 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  61. 61. Functional bias in feedback loop [Hofmann et al. 2014] ● Context: how a recommender systems evaluation based on implicit feedback relates to rating-based evaluation, and how evaluation outcomes may be affected by bias in user behavior ● Findings: ○ implicit and explicit evaluation agree well when assumptions agree well (e.g., precision@10) ○ match between assumption on user behavior and explicit evaluation matters – if assumptions are violated, the wrong recommender can be preferred 61 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  62. 62. ● Context: characterize the impact of human-system feedback loop in the context of recommender systems, demonstrating the unintended consequences of algorithmic confounding ● Findings: ○ the recommendation feedback loop causes homogenization of user behavior ○ users experience losses in utility due to homogenization effects ○ the feedback loop amplifies the impact of recommender systems on the distribution of item consumption Impact on homogeneity in the loop [Chaney et al. 2018] 62 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  63. 63. Functional aspects influence choices [Adomavicius et al 2013] ● Context: explore how consumer preferences are impacted by predictions of recommender systems ● Findings: ○ the rating presented by a recommender serves as an anchor for the consumer’s preference ○ viewers’ preference ratings can be significantly influenced by the recommendation received ○ the effect is sensitive to the perceived reliability of a recommender system ○ the effect of anchoring is continuous and linear, operating over a range of system perturbations 63 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  64. 64. Missing data biases internal functions [Pradel et al. 2012] ● Context: study two major biases of the selection of items, i.e., some items obtain more ratings than others (popularity) and positive ratings are observed more frequently than negative ratings (positivity) ● Findings: ○ considering missing data as a form of negative feedback during training may improve performances ○ ...but it can be misleading when testing, favoring popularity more than user preferences 64 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  65. 65. Deceptive expectations bias user views [Elsweiler et al. 2017] ● Context: they explore the feasibility of substituting meals that would typically be recommended to users with similar, healthier dishes, investigating how people perceive and select recipes ● Findings: ○ participants are unable to reliably identify which recipe contains most fat due to their answers being biased by lack of information ○ perception of fat content can be influenced by the information available and, in some cases, misleading cues (image or title) can bias a false impression 65 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  66. 66. Example of external bias [Jahanbakhsh et al. 2020] ● Context: study how the ratings people receive on online labor platforms are influenced by their performance, gender, their rater’s gender, and displayed ratings from other raters ● Findings: ○ when the performance of paired workers was similar, low-performing females were rated lower than their male counterparts ○ where there was a clear performance difference between paired workers, low-performing females were preferred over a similarly-performing males ○ displaying an average rating from other raters made ratings more extreme, resulting in high performing workers receiving significantly higher ratings 66 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  67. 67. Bias introduced by external viewpoints [Schwind et al. 2012] ● Context: when a diversity of viewpoints is available, learners prefer information that is consistent with their prior preferences; so, they investigated the role of two potential moderators on: ○ confirmation bias: the tendency to select more preference-consistent information ○ evaluation bias: the tendency to evaluate preference-consistent information as better ● Findings: ○ preference-inconsistent recommendations can be used to overcome this bias ○ preference-inconsistent recommendations reduced confirmation bias irrespective of prior knowledge; evaluation bias was only reduced for participants with no prior knowledge ○ preference-inconsistent recommendations led to reduced confirmation bias under cooperation and under competition; evaluation bias was only reduced under cooperation. 67 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  68. 68. On decision biases in user's choices [Teppan and Zanker 2015] ● Context: experimental analysis of the impact of different decision biases like decoy or position effects, as well as risk aversion in positive decision frames ● Findings: risk aversion can be observed in all settings, while position and decoy effects only play a role when risk aversion is not too predominant 68 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  69. 69. Question time 69 How can bias be emphasized in the recommendation pipeline? Please, provide an example source of bias for each of the following steps: A. Data collection: ... B. Data preparation: ... C. Model optimization: ... D. Model evaluation: ... E. Recommendation delivering: ...
  70. 70. Data Acquisition and Storage Recommendation pipeline: data 70 Data Data Preparation Advances in Bias-aware Recommendation on the Web Boratto and Marras Pre- Processed Data Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  71. 71. Sources of bias in data collection [Olteanu et al. 2017] ● Data acquisition ○ discouraging data collection by third parties ○ programmatic limitation of access to data (e.g., time, amount, size) ○ not all relevant data captured by the platform or opaque and unclear sampling strategies ● Data querying ○ limited expressiveness of APIs regarding information needs ○ different ways of operationalization of information by APIs ○ influence of keywords on datasets in keyword-based queries ● Data filtering ○ removal of outliers that are relevant for the analysis ○ bounding of analysis due to text filtering operations 71 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  72. 72. Sources of bias in data preparation [Olteanu et al. 2017] ● Data cleaning ○ data representation choices and default values ○ normalization procedures (e.g., based on geographical information) ● Data enrichment ○ subjective and noisy labels due to manual annotations ○ errors due to automatic annotation based on statistical or machine learning ● Data aggregation ○ lose of information due to high-level aggregation ○ spurious patterns of association when data is groups based on certain attributes 72 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  73. 73. Recommendation pipeline: model 73 Model Recommendations Model Prediction Recommendation Delivering Model Evaluation Advances in Bias-aware Recommendation on the Web Boratto and Marras Model Setup and Training Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  74. 74. Sources of bias in model exploitation [Olteanu et al. 2017] ● Qualitative analysis ○ data representation choices and default values ○ normalization procedures (e.g., based on geographical information) ● Descriptive analysis ○ research often relying on counting entities or influence of bias and confounders on correlation analysis ● Inference analysis ○ definition of target variables, class labels, or data representations ○ effect of the objective function to the inference task ● Observational analysis ○ peer effects due to platform affordances and conventions ○ selection bias and how treatment effects on results generalizability 74 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  75. 75. Sources of bias in model evaluation [Olteanu et al. 2017, Bellogín et al. 2017] ● Evaluation data selection ○ imbalances of data samples due to their popularity ○ sensitivity to the ratio of the test ratings versus the added non-relevant items ● Metrics selection ○ influence of choice of metrics on research study takeaways ○ accounting domain impact throughout performance assessment ● Result assessment and interpretation ○ traces and patterns changing with context ○ going beyond studies evaluated on a single dataset or method 75 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  76. 76. Bias on popularity in evaluation [Bellogín et al. 2017] 76 Advances in Bias-aware Recommendation on the Web Boratto and Marras ● First percentile-based approach: ○ dividing items in m popularity percentiles ○ breaking down the computation of accuracy by such percentiles ○ averaging the m obtained values ● Second uniform test approach: ○ formation of data splits where all items have the same amount of test ratings ○ picking a set T of candidate items and a number g of test ratings per item Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  77. 77. Bias and random decoys in evaluation [Ekstrand et al. 2017] [Other readings: Lim et al. 2015, Yang et al. 2018, Carraro et al. 2020] ● Context: examine the random decoys protocol, where the candidate set consists of the test set items plus a randomly-selected set of N decoy items ● Findings: ○ the distribution of items goodness required to avoid misclassified decoys with reasonable probability is unreasonable ○ there is a serious discrepancy between theoretical and observed behavior of the random decoy strategy with respect to popularity bias 77 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  78. 78. Bias on error estimation in evaluation [Tian et al. 2020] ● Context: offline evaluation cannot accurately assess novel, relevant recommendations; the most novel items are missing from the data and cannot be judged as relevant ● Findings: ○ missing data in the observation process causes the evaluation to mis-estimate metric values ○ substantial breakthroughs in recommendation quality will be difficult to be assessed offline 78 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias through the pipeline
  79. 79. Bias-aware process pipeline 79 IDENTIFY PRODUCT GOALS ● What are you trying to achieve? ● For what population of people? ● What metrics are you tacking? MITIGATE ISSUES ● Does data include enough minority samples? ● Do our proxies measure what we think they do? ● Does the bias notion capture stakeholders’ needs? IDENTIFY STAKEHOLDERS ● Who has a stake in this product? ● Who might be harmed? ● How? DEVELOP AND ANALYZE THE SYSTEM ● How well the system matches product goals? ● To what degree bias is still present? ● How decisions impact on each stakeholder? DEFINE A BIAS NOTION ● What type of bias? At what point? ● What distributions? Bias-aware process pipeline Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias Mitigation
  80. 80. Techniques for bias treatment 80 Pre-processing before model training In-processing during model training Post-processing after model training Pre-processing techniques try to transform the data so that the bias is mitigated. If the algorithm is allowed to modify the training data, pre-processing can be used In-processing techniques try to modify learning algorithms to mitigate bias during training process. If it is allowed to change the learning procedure, in-processing can be used Post-processing is performed by re-ranking items of the lists obtained after model training. If the algorithm can treat the learned model as a black box, post-processing can be used Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias Mitigation
  81. 81. Example of pre-processing treatment [Jannach et al. 2015] ● Idea: extending BPR with a modified distribution function φ that samples tuples (u,i, j) where i is less popular and j is more popular ● Algorithms: BPR ● Findings: it is possible to mitigate popularity bias, with minor losses in effectiveness 81 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias Mitigation
  82. 82. Example of in-processing treatment [Abdollahpouri et al. 2017] ● Idea: identify a regularization component of the objective to be minimized when the distribution of recommendations achieves a 50/50 balance between medium-tail and short-head items. ● Algorithms: RankALS (i.e., pair-wise learning) ● Findings: it is possible to model the trade-off between long-tail catalog coverage and ranking accuracy as a multi-objective optimization problem based on a dissimilarity matrix 82 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias Mitigation
  83. 83. Example of post-processing treatment [Abdollahpouri et al. 2019] ● Idea: they modified the xQuAD to produce a new re-ranked list S (|S | < |R|) that manages popularity bias while still being accurate ● Algorithms: RankALS (i.e., pair-wise learning) ● Findings: on two datasets, the re-ranking methods boost long-tail items while keeping the accuracy loss small, compared to the model-based technique 83 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias Mitigation
  84. 84. Other treatments for popularity bias among others... ● Treatments that manipulate interactions before training a model: ○ sample tuples (u,i, j) where i is less popular than j for pair-wise learning [Jannach et al. 2014] ○ remove popular items, simulating situations in which these items are missing [Cremonesi et al. 2014] ○ detect and fix noisy ratings by characterizing items and users by their profiles [Toledo et al. 2015] ● Treatments that regularize the loss function score during training: ○ a regularization that balance recommendation accuracy and intra-list diversity [Abdollahpouri et al. 2017] ○ a regularization that minimizes the correlation between accuracy and item popularity [Boratto et al. 2020a] ○ adversarial framework: minimax game between the BPR model and a discriminator [Zhu et al. 2020] ● Treatments that re-rank items after model training: ○ two-way aggregation of direct and reversed rank results (to improve coverage and accuracy) [Dong et al. 2020] ○ a re-ranking that suggests first items from unseen providers (to improve coverage) [Burke et al. 2016] ○ a re-ranking score that balances predicted rating with the inverse of popularity [Abdollahpouri et al. 2018] ○ a re-ranking that includes long-tail items the user might like [Abdollahpouri et al. 2019] 84 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias Mitigation
  85. 85. Treatments against other biases (1) ● Biases related to how items are sampled, positioned, and/or selected, e.g.: ○ connect recommendation to causal inference from experimental and observational data [Schnabel et al. 2016] ○ integrate imputed errors and propensities, for alleviating the effect of propensity variance [Wang et al. 2019] ○ manage the spiral of silence effect, i.e., users are likely to rate if they perceive a support by the dominant opinion [Liu D. et al. 2019a] ○ estimate item frequency from a data stream, subject to vocabulary and distribution shifts [Yi et al. 2019] ○ model position-bias offline and conduct online inference without position information [Guo et al. 2019] ○ off-policy correction to learn from feedback given by an ensemble of prior model policies [Chen et al. 2019a] ○ a clipped estimator to improve the bias-variance trade-off than w.r.t. unbiased estimator [Saito et al. 2020] ○ a counterfactual approach which accounts for selection and position bias jointly [Ovaisi et al. 2020] ○ a two-stage off-policy that takes the model into account while training the candidate model [Ma et al. 2020] 85 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias Mitigation
  86. 86. Treatments against other biases (2) ● Biases associated to how items reach their audience: ○ a novel probabilistic method for weighted sampling of k neighbors that considers the similarity levels between the target user (or item) and the candidate neighbors [Adamopoulos et al. 2014] ○ a target customer re-ranking algorithm to adjust the population distribution and composition in the top-k target customers of an item while maintaining recommendation quality [Zhao et al. 2020] ● Biases associated to how items are marketed, e.g.: ○ a fairness-aware framework to address market imbalance bias by calibrating the parity of prediction errors across different market segments [Wan et al. 2020] 86 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias Mitigation
  87. 87. Treatments against other biases (3) ● Biases associated to reviews and textual opinions, e.g.: ○ a sentiment classification scoring method, which employs dual attention vectors to predict the users’ sentiment scores of their reviews , to catch opinion bias and enhance user-item matrix [Xu et al. 2018] ○ a hybrid model that integrates modified-sied information related to textual bias and rating bias in matrix factorization, getting a specific word representation for each item review [Dai et al. 2018] 87 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias Mitigation
  88. 88. Treatments against other biases (4) ● Biases associated to social trust and influence: ○ a mitigation using polynomial regression and a Bayesian information criterion to predict ratings less influenced by the tendency to conform to the perceived “norm” in a community [Krishnan et al. 2014] ○ clustering user-item space to discover rating bubbles derived from the theory of social bias, i.e., existing ratings indirectly influences the users' opinion to follow the herd instinct [Divyaa et al. 2019] ○ a matrix completion algorithm that performs hybrid memory-based collaborative filtering, improving how the bribery effect is managed and how the system is robust against bribery [Ramos et al. 2020b] 88 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias Mitigation
  89. 89. Treatments against other biases (5) ● Biases related to the interactions of users over time, e.g.: ○ an historical influence-aware latent factor model to capture and mitigate historical distortions in each single rating under the assimilation-contrast theory: users conform to historical ratings if historical ratings are not far from the product quality (assimilation), while users deviate from historical ratings if historical ratings are significantly different from the product quality (contrast) [Zhang et al. 2018] ○ an unbiased loss using inverse propensity weighting, that includes the recency propensity of item x at time t, to be used in point-wise learning to rank [Chen et al. 2019b] 89 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Bias Mitigation
  90. 90. Part II.II Hands on Item Popularity Bias
  91. 91. Steps of this hands on 91 1 Model Exploration We consider on data and models introduced in the first hands on to inspect of popularity impacts on visibility and exposure of items Mitigation Setup We arrange a representative set of mitigation strategies against popularity bias in pre-, in- and post-processing 2 Mitigation Running We run the mitigation procedure, inspecting how the optimization processes influences popularity values 3 Model Re-Evaluation We re-run the evaluation of the first hands on to highlight how disparities among popular and unpopular items are reduced 4 Impact Assessment We interpret the results obtained during evaluation in order to envision how stakeholders are impacted 5 Advances in Bias-aware Recommendation on the Web Boratto and Marras Investigation on item popularity bias https://colab.research.google.com/github/biasinrecsys/wsdm2021/blob/master/notebooks/item_popularity_bias.ipynb
  92. 92. Session III Unfairness Mitigation
  93. 93. Part III.I Discrimination
  94. 94. Biases that lead to discrimination [Mehrabi et al. 2019] 94 Advances in Bias-aware Recommendation on the Web Boratto and Marras Direct Discrimination direct discrimination happens when protected attributes of groups or individuals explicitly result in non-favorable outcomes toward them Indirect Discrimination individuals appear to be treated based on neutral and non-protected attributes; however, protected groups or individuals get to be treated unjustly as a result of implicit effects from protected attributes Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Discrimination
  95. 95. Granularity of discrimination [Mehrabi et al. 2019] 95 Advances in Bias-aware Recommendation on the Web Boratto and Marras Individual Discrimination when a system gives unfairly different predictions to individuals who are considered similar for that task Group Discrimination when a system systematically treats individuals who belong to different groups unfairly Sub-group Discrimination when a system systematically discriminates individuals over a large collection of subgroups Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Discrimination
  96. 96. Contextualizing to recommendation ● Individual and sub-group discrimination are very challenging to assess: user similarity at individual level should be on intrinsic properties that characterize the users and not based on behavioral aspects ○ No study so far in the recommender system domain ○ Advances in ranking systems, where recommendation approaches can draw from: [Zehlike et al. 2017, Celis et al. 2017, Biega et al. 2018, Lahoti et al. 2019, Yada et al. 2019, Singh and Joachims 2019, Kuhlman et al. 2019, Zehlike and Castillo 2020, Ramos and Boratto 2020a, Diaz et al. 2020] ○ For much more, see the "Fairness & Discrimination in Retrieval & Recommendation" tutorial at https://fair-ia.ekstrandom.net/sigir2019-slides.pdf ● Group discrimination is much more common (e.g., how do the generated recommendation reflect the needs of consumers/providers of different genders?) 96 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Discrimination
  97. 97. Types of disparity in groups [Mehrabi et al. 2019] 97 Advances in Bias-aware Recommendation on the Web Boratto and Marras Disparate Treatment when members of different groups are treated differently Disparate Impact when members of different groups obtain different outcomes Disparate Mistreatment when members of different groups have different error rates Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Discrimination
  98. 98. Contextualizing disparities ● Recommender systems usually do not receive as input any sensitive attribute of the user, so disparate treatment is usually not considered ● Disparate impact usually does not affect recommender systems: two users of different genders with the same ratings for the same items, usually receive the same recommendations ● Disparate mistreatment means that groups of users with different sensitive attributes receive different recommendation quality. Known as consumer fairness in the RecSys domain (presented later) 98 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Discrimination
  99. 99. Definitions of fairness [Mehrabi et al. 2019] 99 Advances in Bias-aware Recommendation on the Web Boratto and Marras Equalized Odds an algorithm is fair if the groups have equal rates for true positives and false positives Fairness through Awareness an algorithm is fair if it gives similar predictions to similar individuals Equal Opportunity an algorithm is fair if the groups have equal true positive rates Fairness through Unawareness an algorithm is fair as long as any protected attribute is not explicitly used in the decision-making process Demographic Parity an algorithm is fair if the likelihood of a positive outcome should be the same regardless of the group Equality of Treatment an algorithm is fair if the ratio of false negatives and false positives is the same for all the groups Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Discrimination
  100. 100. Fairness metrics for ranked outputs (I) [Yang and Stoyanovich 2017, Farnadi et al. 2018, Sonboli et al. 2019, Deldjoo et al. 2020] ● Normalized discounted difference (rND) computes the difference in the proportion of members of the protected group at top-i and in the over-all population ● Normalized discounted KL-divergence (rKL) measures the expectation of the logarithmic difference between two discrete probability distributions ● Local fairness supports the fact that fairness may be local and the identification of protected groups is only possible through consideration of local conditions 100 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Discrimination
  101. 101. Fairness metrics for ranked outputs (II) [Yang and Stoyanovich 2017, Farnadi et al. 2018, Sonboli et al. 2019, Deldjoo et al. 2020] ● Non-parity unfairness measures the absolute difference between the overall average ratings of users belonging to the unprotected and protected groups ● Value unfairness measures the inconsistency in signed estimation error across the protected and unprotected groups (becomes larger when predictions for one group are overestimated) ● Absolute unfairness measures the inconsistency in absolute estimation error across user groups (becomes large if one group consistently receives more accurate recommendations) ● Csiszar generalized measure of divergence compares the probability distribution of the model performance and a fair probability distribution 101 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Discrimination
  102. 102. Multi-sided fairness [Burke et al. 2017] 102 Advances in Bias-aware Recommendation on the Web Boratto and Marras Consumer-Provider Fairness It might be needed that the platform guarantees fairness for both consumers and providers, e.g.,: ● people matching ● property/business recommendation ● user skills and job matching ● and so on... Consumer Fairness We talk about unfairness for consumers when their experience in platform differs: ● in terms of service effectiveness (results’ relevance, user satisfaction) ● resulting outcomes (exposure to lower-paying job offers) ● participation costs (privacy risks) Provider Fairness Providers experience unfairness when a platform/service creates: ● different opportunities for their items to be consumed ● different visibility or exposure in the ranking ● different participation costs Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Discrimination
  103. 103. Impact of bias on consumer fairness (I) [Ekstrand et al. 2018a] ● Context: they investigate whether demographic groups obtain different utility from recommender systems in LastFM and Movielens 1M datasets ● Algorithms: Popular, Mean Item-Item, User-User, FunkSVD ● Findings: ML1M & LFM1K have statistically-significant differences between gender groups, and LFM360K has significant differences between age brackets 103 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Discrimination
  104. 104. ● Context: investigate three user groups from Last.fm based on how much their listening preferences deviate from the most popular music: (i) low-, (ii) medium-, and (iii) high-mainstream users ● Findings: low-mainstreaminess group significantly receives the worst recommendations Impact of bias on consumer fairness (II) [Kowald et al. 2020] 104 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Discrimination
  105. 105. Impact of bias on provider fairness [Ekstrand et al. 2018b] ● Context: examine the response of collaborative filtering algorithms to the distribution of their input data, with respect to content creators’ gender ● Findings: matrix factorization produced reliably male-biased recommendations, while nearest-neighbor and hierarchical Poisson factorization techniques were closer to the user profile tendency while being less diffuse than their inputs 105 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Discrimination
  106. 106. Have your say 106 Tell us in a sentence up to 3 examples of domains or tasks you work on and how they showed unfairness
  107. 107. Example of pre-processing treatment [Rastegarpanah et al. 2019] ● Idea: augmenting the input with additional data can improve the social desirability of recommendations ● Algorithms: MF family of algorithms ● Findings: the small amounts of antidote data (typically on the order of 1% new users) can generate a dramatic improvement (on the order of 50%) in the polarization or the fairness of the system’s recommendations 107 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Mitigation design
  108. 108. Example of in-processing treatment [Beutel et al. 2019] ● Idea: propose a metric for measuring fairness based on pairwise comparisons; a correlation-based approach to improve performance for the given fairness metric ● Algorithms: learning-to-rank (e.g., point- and pair-wise) ● Findings: while the regularization decreases the pairwise accuracy of the non-subgroup items, it closes the gap in the inter-group pairwise fairness, resulting in only a 2.6% advantage for non-subgroup items in inter-group pairwise fairness, down from 35.6% 108 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Mitigation design
  109. 109. Example of post-processing treatment [Liu et al. 2019b] ● Idea: combining of a personalization-induced term and a fairness-induced term, promoting the loans that belong to currently uncovered borrower groups ● Algorithms: RankSGD, UserKNN, WRMF, Maxent ● Findings: they find a balance between the two terms, where nDCG still remains at a high level after the re-ranking, while fairness of the recommendation is significantly improved, as loans belonging to less-popular groups are promoted. 109 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Mitigation design
  110. 110. Other treatments against C-fairness among others... ● Strategies that introduce regularization or constraints during training: ○ create a balanced neighborhood from protected and unprotected classes [Burke et al. 2018] ○ objective pushing independence between predicted ratings and sensitive attributes [Kamishima et al. 2018] ○ a fairness-aware model that isolates and extracts sensitive information from latent factor matrices [Zhu et al. 2018] ○ a probabilistic programming approach for building fair hybrid recommender systems [Farnadi et al. 2018] ● Strategies that require a re-ranking of the items after training: ○ a heuristic re-ranking to mitigate unfairness in explainable recommendation over knowledge graphs [Fu et al. 2020] ○ a re-ranking to provide fair recommendations to groups [Kaya et al. 2020] 110 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Mitigation design
  111. 111. Treatments for C-fairness of groups among others... ● Stratigi et al. 2017: ○ improving the opportunities that patients have to inform themselves online about diseases and possible treatments ○ identify the correct set of similar users for a user in question, considering health information and ratings ○ a fairness-aware recommendation model of items that are relevant and satisfy the majority of the group members ● Lin et al. 2017: ○ maximize the satisfaction of each group member while minimizing the unfairness between them ○ introduce two concepts of social welfare and fairness, modeling overall utilities and balance between group members ○ an optimization framework for fairness-aware group recommendation from the perspective of Pareto Efficiency ● Serbos et al. 2017: ○ recommending packages of items to groups of users, e.g., recommending vacation packages to groups of tourists ○ fair in the sense that every group member is satisfied by a sufficient number of items in the package ○ propose greedy algorithms that find approximate solutions to meet a better trade-off within reasonable time 111 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Mitigation design
  112. 112. Other treatments against P-fairness among others... ● Strategies involving some degree of pre-processing: ○ a mitigation approach that relies on tailored upsampling in pre-processing of interactions involving minority groups of providers [Boratto et al. 2020b] ● Strategies that change or regularize the algorithm learning process: ○ the concept of balanced neighborhood from protected and unprotected class can be applied also to improve fairness across providers [Burke et al. 2018] ○ a correlation-based regularization that minimizes the correlation between the residual between the clicked and unclicked item and the group membership of the clicked item [Beutel et al. 2019] ● Strategies that re-rank items with a post-processing procedure: ○ a re-ranking to improve exposure distribution across creators, controlling divergence between the desired distribution and the actual obtained distribution of exposure [Modani et al. 2017] ○ iteratively generate the ranking list by trading off between accuracy and the coverage of the providers based on the adaptation of the xQuad algorithm [Liu et al. 2019b] 112 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Mitigation design
  113. 113. Treatments with a multi-sided focus among others... ● In a speed-dating domain: ○ in a speed-dating context, a multi-dimensional utility framework which analyzes the relationship between utilities and recommendation performance, achieving a trade-off [Zheng et al. 2018] ○ an approach to rerank the recommendation list by optimizing (1) the disparity of service; (2) the similarity of mutual preference; (3) the equilibrium of demand and supply [Xia et al. 2019] ● Or in a generic multi-sided marketplace: ○ an integer linear programming-based optimization to deploy changes incrementally in steps, ensuring smooth transition of item exposure and a minimum loss in utility [Patro et al. 2019] ○ an algorithm guarantees at least maximin share of exposure for most of the producers and envy-free up to one good fairness for every customer [Patro et al. 2020] 113 Advances in Bias-aware Recommendation on the Web Boratto and Marras Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals Data and algorithmic bias fundamentals > Mitigation design
  114. 114. Part III.II Hands on Item Provider Fairness
  115. 115. Steps of this hands on 115 1 Model Exploration We consider on data and models introduced in the first hands on to inspect of popularity impacts on visibility and exposure of providers Mitigation Setup We arrange a representative set of mitigation strategies against provider unfairness in pre-, in- and post-processing 2 Mitigation Running We run the mitigation procedure, inspecting how the optimization processes influences provider fairness 3 Model Re-Evaluation We re-run the evaluation of the first hands on to highlight how disparities among providers are reduced 4 Impact Assessment We interpret the results obtained during evaluation in order to envision how stakeholders are impacted 5 Advances in Bias-aware Recommendation on the Web Boratto and Marras Investigation on item provider fairness https://colab.research.google.com/github/biasinrecsys/wsdm2021/blob/master/notebooks/item_provider_fairness.ipynb
  116. 116. Part IV Concluding Remarks
  117. 117. Contextual challenges 117 Advances in Bias-aware Recommendation on the Web Boratto and Marras ● Stakeholders have different (and conflicting) needs. How can recommender systems account for them? ● Multi-disciplinary approaches to go beyond algorithms (e.g., to link justice and fairness) ● Synthesizing a definition of bias or fairness is challenging ● Creating a common vocabulary to recognize different types of bias and unfairness ● Data to characterize bias phenomena with enough depth is lacking (especially for sensitive attributes) ● There are forms of bias on the Web that have not been studied in the recommendation literature Final Remarks and Open Discussion
  118. 118. Operational challenges 118 Advances in Bias-aware Recommendation on the Web Boratto and Marras ● Measuring and operationalizing bias or fairness. How can we optimize a recommender system for it? ● Can we mitigate multiple forms of bias at the same time? ● Slight changes throughout the pipeline can make a huge difference on impact ● Research and development should be more focused on the real world application ● When mitigating bias we usually trade for other qualities. ● How can we mitigate bias without compromising recommendation quality? Final Remarks and Open Discussion
  119. 119. Evaluation challenges ● What if we do not have the sensitive attributes in the collected data? ● How should we select an approach with respect to another (e.g., equity vs equality)? ● How to identify harms in the considered context? ● Will the chosen offline metrics and experiments lead to the desired results online? ● How to inspect whether data generation and collection methods are appropriate? ● How could we take into account both bias goals and efficiency in the real world? 119 Advances in Bias-aware Recommendation on the Web Boratto and Marras Final Remarks and Open Discussion
  120. 120. Resources from this tutorial 1. Tutorial website https://biasinrecsys.github.io/wsdm2021/ 2. Github repository https://github.com/biasinrecsys/wsdm2021 3. Jupyter notebooks 3.1. https://colab.research.google.com/github/biasinrecsys/wsdm2021/blob/master/notebooks/model_setup.ipynb 3.2. https://colab.research.google.com/github/biasinrecsys/wsdm2021/blob/master/notebooks/item_popularity_bias.ipynb 3.3. https://colab.research.google.com/github/biasinrecsys/wsdm2021/blob/master/notebooks/item_provider_fairness.ipynb 120 Advances in Bias-aware Recommendation on the Web Boratto and Marras Final Remarks and Open Discussion
  121. 121. Your feedback is essential for us! https://forms.gle/HaxAMNwAWxSacDgD9
  122. 122. Questions?
  123. 123. References #1 1. Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, Luiz Augusto Pizzato: Multi Stakeholder recommendation: Survey and research directions. User Model. User Adapt. Interact. 30(1): 127-158 (2020). 2. Himan Abdollahpouri, Robin Burke, Bamshad Mobasher: Managing Popularity Bias in Recommender Systems with Personalized Re-Ranking. FLAIRS Conference 2019: 413-418 (2019). 3. Himan Abdollahpouri, Robin Burke, Bamshad Mobasher. Popularity-Aware Item Weighting for Long-Tail Recommendation. arXiv preprint arXiv:1802.05382 (2018). 4. Himan Abdollahpouri, Robin Burke, Bamshad Mobasher: Controlling Popularity Bias in Learning-to-Rank Recommendation. RecSys 2017: 42-46 (2017). 5. Panagiotis Adamopoulos, Alexander Tuzhilin: On over-specialization and concentration bias of recommendations: probabilistic neighborhood selection in collaborative filtering systems. RecSys 2014: 153-160 (2014). 6. Gediminas Adomavicius, Jesse C. Bockstedt, Shawn P. Curley, Jingjing Zhang: Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects. Inf. Syst. Res. 24(4): 956-975 (2013). 7. Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone, Joseph Trotta: Local Popularity and Time in top-N Recommendation. ECIR (1) 2019: 861-868 (2019). 8. Kenneth C. Arnold, Krysta Chauncey, Krzysztof Z. Gajos: Sentiment Bias in Predictive Text Recommendations Results in Biased Writing. Graphics Interface 2018: 42-49 (2018). 123 Advances in Bias-aware Recommendation on the Web Boratto and Marras
  124. 124. References #2 9. Alejandro Bellogín, Pablo Castells, Iván Cantador: Statistical biases in Information Retrieval metrics for recommender systems. Inf. Retr. J. 20(6): 606-634 (2017). 10. Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow: Fairness in Recommendation Ranking through Pairwise Comparisons. KDD 2019: 2212-2220 11. Asia J. Biega, Krishna P. Gummadi, Gerhard Weikum: Equity of Attention: Amortizing Individual Fairness in Rankings. SIGIR 2018: 405-414 12. Black, J. S., & van Esch, P. (2020). AI-enabled recruiting: What is it and how should a manager use it?. Business Horizons, 63(2), 215-226. 13. Ludovico Boratto, Gianni Fenu, Mirko Marras: The Effect of Algorithmic Bias on Recommender Systems for Massive Open Online Courses. ECIR (1) 2019: 457-472 (2019). 14. Ludovico Boratto, Gianni Fenu, Mirko Marras: Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendation. CoRR abs/2006.04275 (2020a). 15. Ludovico Boratto, Gianni Fenu, Mirko Marras: Interplay between Upsampling and Regularization for Provider Fairness in Recommender Systems. CoRR abs/2006.04279 (2020b). 16. Engin Bozdag: Bias in algorithmic filtering and personalization. Ethics Inf Technol 15, 209–227 (2013). 17. Robin Burke. Multisided fairness for recommendation. arXiv preprint arXiv:1707.00093 (2017). 18. Robin Burke, Nasim Sonboli, Aldo Ordonez-Gauger: Balanced Neighborhoods for Multi-sided Fairness in Recommendation. FAT 2018: 202-214 (2018) 19. Robin D. Burke, Himan Abdollahpouri, Bamshad Mobasher, Trinadh Gupta: Towards Multi-Stakeholder Utility Evaluation of Recommender Systems. UMAP Extended Proceedings (2016). 124 Advances in Bias-aware Recommendation on the Web Boratto and Marras
  125. 125. References #3 20. Rocío Cañamares, Pablo Castells: Should I Follow the Crowd?: A Probabilistic Analysis of the Effectiveness of Popularity in Recommender Systems. SIGIR 2018: 415-424 21. Diego Carraro, Derek Bridge: Debiased offline evaluation of recommender systems: a weighted-sampling approach. SAC 2020: 1435-1442 (2020). 22. L. Elisa Celis, Damian Straszak, Nisheeth K. Vishnoi: Ranking with Fairness Constraints. ICALP 2018: 28:1-28:15 (2017). 23. Roberto Centeno, Ramón Hermoso, Maria Fasli: On the inaccuracy of numerical ratings: dealing with biased opinions in social networks. Inf. Syst. Frontiers 17(4): 809-825 (2015). 24. Allison J. B. Chaney, Brandon M. Stewart, Barbara E. Engelhardt: How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. RecSys 2018: 224-232 (2018). 25. Sushma Channamsetty, Michael D. Ekstrand: Recommender Response to Diversity and Popularity Bias in User Profiles. FLAIRS Conference 2017: 657-660 (2017). 26. Minmin Chen, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, Ed H. Chi: Top-K Off-Policy Correction for a REINFORCE Recommender System. WSDM 2019: 456-464 (2019a). 27. Ruey-Cheng Chen, Qingyao Ai, Gaya Jayasinghe, W. Bruce Croft: Correcting for Recency Bias in Job Recommendation. CIKM 2019: 2185-2188 (2019b). 28. Paolo Cremonesi, Franca Garzotto, Roberto Pagano, Massimo Quadrana: Recommending without short head. WWW (Companion Volume) 2014: 245-246 (2014). 125 Advances in Bias-aware Recommendation on the Web Boratto and Marras
  126. 126. References #4 29. Jiao Dai, Mingming Li, Songlin Hu, Jizhong Han: A Hybrid Model Based on the Rating Bias and Textual Bias for Recommender Systems. ICONIP (2) 2018: 203-214 (2018). 30. Sarah Dean, Sarah Rich, Benjamin Recht: Recommendations and user agency: the reachability of collaboratively-filtered information. FAT* 2020: 436-445 (2020). 31. Yashar Deldjoo, Vito Walter Anelli, Hamed Zamani, Alejandro Bellogín, Tommaso DiNoia - A Flexible Framework for Evaluating User and Item Fairness in Recommender Systems. In User Modeling and User-Adapted Interaction (2020) 32. Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J. Biega, Ben Carterette: Evaluating Stochastic Rankings with Expected Exposure. CoRR abs/2004.13157 (2020). 33. Divyaa L. R., Nargis Pervin: Towards generating scalable personalized recommendations: Integrating social trust, social bias, and geo-spatial clustering. Decis. Support Syst. 122 (2019). 34. Qiang Dong, Quan Yuan, Yang-Bo Shi: Alleviating the recommendation bias via rank aggregation. Physica A: Statistical Mechanics and its Applications, 534, 122073. (2019). 35. Bora Edizel, Francesco Bonchi, Sara Hajian, André Panisson, Tamir Tassa: FaiRecSys: mitigating algorithmic bias in recommender systems. Int. J. Data Sci. Anal. 9(2): 197-213 (2020) 36. David Elsweiler, Christoph Trattner, Morgan Harvey: Exploiting Food Choice Biases for Healthier Recipe Recommendation. SIGIR 2017: 575-584 (2017). 37. Michael D. Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D. Ekstrand, Oghenemaro Anuyah, David McNeill, Maria Soledad Pera: All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness. FAT 2018: 172-186 (2018a). 126 Advances in Bias-aware Recommendation on the Web Boratto and Marras
  127. 127. References #5 38. Michael D. Ekstrand, Mucun Tian, Mohammed R. Imran Kazi, Hoda Mehrpouyan, Daniel Kluver: Exploring author gender in book rating and recommendation. RecSys 2018: 242-250 (2018b) 39. Michael D. Ekstrand, Vaibhav Mahant: Sturgeon and the Cool Kids: Problems with Random Decoys for Top-N Recommender Evaluation. FLAIRS Conference 2017: 639-644 (2017). 40. Eslami, M., Vaccaro, K., Lee, M. K., Elazari Bar On, A., Gilbert, E., & Karahalios, K. (2019, May). User attitudes towards algorithmic opacity and transparency in online reviewing platforms. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-14). 41. Francesco Fabbri, Francesco Bonchi, Ludovico Boratto, Carlos Castillo: The Effect of Homophily on Disparate Visibility of Minorities in People Recommender Systems. ICWSM 2020: 165-175 (2020). 42. Golnoosh Farnadi, Pigi Kouki, Spencer Thompson, Sriram Srinivasan, Lise Getoor: A Fairness-aware Hybrid Recommender System. CoRR abs/1809.09030 (2018). 43. Zuohui Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao, Qiaoying Huang, Yingqiang Ge, Shuyuan Xu, Shijie Geng, Chirag Shah, Yongfeng Zhang, Gerard de Melo: Fairness-Aware Explainable Recommendation over Knowledge Graphs. CoRR abs/2006.02046 (2020). 44. Huifeng Guo, Jinkai Yu, Qing Liu, Ruiming Tang, Yuzhou Zhang: PAL: a position-bias aware learning framework for CTR prediction in live recommender systems. RecSys 2019: 452-456 (2019). 45. Fangjian Guo, David B. Dunson: Uncovering Systematic Bias in Ratings across Categories: a Bayesian Approach. RecSys 2015: 317-320 (2015). 46. Katja Hofmann, Anne Schuth, Alejandro Bellogín, Maarten de Rijke: Effects of Position Bias on Click-Based Recommender Evaluation. ECIR 2014: 624-630 (2014). 47. Longke Hu, Aixin Sun, Yong Liu: Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction. SIGIR 2014: 345-354 (2014). 127 Advances in Bias-aware Recommendation on the Web Boratto and Marras
  128. 128. References #6 48. Farnaz Jahanbakhsh, Justin Cranshaw, Scott Counts, Walter S. Lasecki, Kori Inkpen: An Experimental Study of Bias in Platform Worker Ratings: The Role of Performance Quality and Gender. CHI 2020: 1-13 (2020). 49. Dietmar Jannach, Lukas Lerche, Iman Kamehkhosh, Michael Jugovac: What recommenders recommend: an analysis of recommendation biases and possible countermeasures. User Model. User Adapt. Interact. 25(5): 427-491 (2015). 50. Marius Kaminskas, Derek Bridge: Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems. ACM Trans. Interact. Intell. Syst. 7(1): 2:1-2:42 (2017). 51. Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, Jun Sakuma: Recommendation Independence. FAT 2018: 187-201 (2018). 52. Mesut Kaya, Derek Bridge, and Nava Tintarev. 2020. Ensuring Fairness in Group Recommendations by Rank-Sensitive Balancing of Relevance. In Fourteenth ACM Conference on Recommender Systems (RecSys '20). 53. Dominik Kowald, Markus Schedl, Elisabeth Lex: The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study. ECIR (2) 2020: 35-42 (2020). 54. Sanjay Krishnan, Jay Patel, Michael J. Franklin, Ken Goldberg: A methodology for learning, analyzing, and mitigating social influence bias in recommender systems. RecSys 2014: 137-144 (2014). 55. Caitlin Kuhlman, MaryAnn Van Valkenburg, Elke A. Rundensteiner: FARE: Diagnostics for Fair Ranking using Pairwise Error Metrics. WWW 2019: 2936-2942 (2019). 56. Preethi Lahoti, Krishna P. Gummadi, Gerhard Weikum: iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making. ICDE 2019: 1334-1345 128 Advances in Bias-aware Recommendation on the Web Boratto and Marras
  129. 129. References #7 57. Dokyun Lee, Kartik Hosanagar: How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment. Inf. Syst. Res. 30(1): 239-259 (2019). 58. Jurek Leonhardt, Avishek Anand, Megha Khosla: User Fairness in Recommender Systems. WWW (Companion Volume) 2018: 101-102 (2018). 59. Mingming Li, Jiao Dai, Fuqing Zhu, Liangjun Zang, Songlin Hu, Jizhong Han: A Fuzzy Set Based Approach for Rating Bias. AAAI 2019: 9969-9970 (2019). 60. Daryl Lim, Julian J. McAuley, Gert R. G. Lanckriet: Top-N Recommendation with Missing Implicit Feedback. RecSys 2015: 309-312 (2015). 61. Xiao Lin, Min Zhang, Yongfeng Zhang, Zhaoquan Gu, Yiqun Liu, Shaoping Ma: Fairness-Aware Group Recommendation with Pareto-Efficiency. RecSys 2017: 107-115 (2017). 62. Kun Lin, Nasim Sonboli, Bamshad Mobasher, Robin Burke: Crank up the Volume: Preference Bias Amplification in Collaborative Recommendation. RMSE@RecSys 2019 (2019). 63. Dugang Liu, Chen Lin, Zhilin Zhang, Yanghua Xiao, Hanghang Tong: Spiral of Silence in Recommender Systems. WSDM 2019: 222-230 (2019a). 64. Weiwen Liu, Jun Guo, Nasim Sonboli, Robin Burke, Shengyu Zhang: Personalized fairness-aware re-ranking for microlending. RecSys 2019: 467-471 (2019b). 65. Jiaqi Ma, Zhe Zhao, Xinyang Yi, Ji Yang, Minmin Chen, Jiaxi Tang, Lichan Hong, Ed H. Chi: Off-policy Learning in Two-stage Recommender Systems. WWW 2020: 463-473 (2020). 66. Benjamin M. Marlin, Richard S. Zemel, Sam T. Roweis, Malcolm Slaney: Recommender Systems, Missing Data and Statistical Model Estimation. IJCAI 2011: 2686-2691 (2011). 129 Advances in Bias-aware Recommendation on the Web Boratto and Marras
  130. 130. References #8 68. Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, Aram Galstyan. A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635 (2019). 69. Rishabh Mehrotra, James McInerney, Hugues Bouchard, Mounia Lalmas, Fernando Diaz: Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems. CIKM 2018: 2243-2251 (2018). 70. Silvia Milano, Mariarosaria Taddeo, Luciano Floridi. Recommender systems and their ethical challenges. AI & Soc (2020). 71. Natwar Modani, Deepali Jain, Ujjawal Soni, Gaurav Kumar Gupta, Palak Agarwal: Fairness Aware Recommendations on Behance. PAKDD (2) 2017: 144-155 (2017). 72. Azadeh Nematzadeh, Giovanni Luca Ciampaglia, Filippo Menczer, Alessandro Flammini: How algorithmic popularity bias hinders or promotes quality. CoRR abs/1707.00574 (2017). 73. Alexandra Olteanu, Carlos Castillo, Fernando Diaz, Emre Kiciman: Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries. Frontiers Big Data 2: 13 (2019) 74. Zohreh Ovaisi, Ragib Ahsan, Yifan Zhang, Kathryn Vasilaky, Elena Zheleva: Correcting for Selection Bias in Learning-to-rank Systems. WWW 2020: 1863-1873 (2020). 75. Papakyriakopoulos, Orestis, Juan Carlos Medina Serrano, and Simon Hegelich. "Political communication on social media: A tale of hyperactive users and bias in recommender systems." Online Social Networks and Media 15 (2020): 100058. 76. Anish Anil Patankar, Joy Bose, Harshit Khanna: A Bias Aware News Recommendation System. ICSC 2019: 232-238 (2019). 77. Bruno Pradel, Nicolas Usunier, Patrick Gallinari: Ranking with non-random missing ratings: influence of popularity and positivity on evaluation metrics. RecSys 2012: 147-154 (2012). 130 Advances in Bias-aware Recommendation on the Web Boratto and Marras
  131. 131. References #9 78. Gourab K. Patro, Arpita Biswas, Niloy Ganguly, Krishna P. Gummadi, Abhijnan Chakraborty: FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms. WWW 2020: 1194-1204 (2020). 79. Gourab K. Patro, Abhijnan Chakraborty, Niloy Ganguly, Krishna Gummadi. Incremental Fairness in Two-Sided Market Platforms: On Smoothly Updating Recommendations. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 01, pp. 181-188). 80. Selwyn Piramuthu, Gaurav Kapoor, Wei Zhou, Sjouke Mauw: Input online review data and related bias in recommender systems. Decis. Sup. Sys. 53(3): 418-424 (2012). 81. Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020, January). Mitigating bias in algorithmic hiring: Evaluating claims and practices. In Proceedings of the 2020 conference on fairness, accountability, and transparency (pp. 469-481). 82. Guilherme Ramos and Ludovico Boratto, Reputation (In)dependence in Ranking Systems: Demographics Influence Over Output Disparities, in Proceedings of the 43rd International ACM SIGIR Conference on Researchand Development in Information Retrieval, SIGIR 2020 (2020a) 83. Guilherme Ramos, Ludovico Boratto, Carlos Caleiro. On the negative impact of social influence in recommender systems: A study of bribery in collaborative hybrid algorithms. Information Processing & Management, 57(2), 102058 (2020). 84. Bashir Rastegarpanah, Krishna P. Gummadi, Mark Crovella: Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems. WSDM 2019: 231-239 85. Yehezkel S. Resheff, Yanai Elazar, Moni Shahar, Oren Sar Shalom: Privacy and Fairness in Recommender Systems via Adversarial Training of User Representations. ICPRAM 2019: 476-482 86. Francesco Ricci, Lior Rokach, Bracha Shapira: Recommender Systems: Introduction and Challenges. Recommender Systems Handbook 2015: 1-34 (2015). 131 Advances in Bias-aware Recommendation on the Web Boratto and Marras
  132. 132. References #10 87. Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, Kazuhide Nakata: Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback. WSDM 2020: 501-509 (2020). 88. Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims: Recommendations as Treatments: Debiasing Learning and Evaluation. ICML 2016: 1670-1679 (2016). 89. Christina Schwind, Jürgen Buder: Reducing confirmation bias and evaluation bias: When are preference-inconsistent recommendations effective - and when not? Comput. Hum. Behav. 28(6): 2280-2290 (2012). 90. Dimitris Serbos, Shuyao Qi, Nikos Mamoulis, Evaggelia Pitoura, Panayiotis Tsaparas: Fairness in Package-to-Group Recommendations. WWW 2017: 371-379 (2017). 91. Shakespeare, D., Porcaro, L., Gómez, E., & Castillo, C. Exploring Artist Gender Bias in Music Recommendation. 92. Ashudeep Singh, Thorsten Joachims: Policy Learning for Fairness in Ranking. NeurIPS 2019: 5427-5437 (2019). 93. Ashudeep Singh, Thorsten Joachims: Fairness of Exposure in Rankings. KDD 2018: 2219-2228 (2018). 94. Nasim Sonboli, Robin Burke: Localized Fairness in Recommender Systems. UMAP (Adjunct Publication) 2019: 295-300 (2019). 95. Harald Steck: Item popularity and recommendation accuracy. RecSys 2011: 125-132 (2011). 96. Maria Stratigi, Haridimos Kondylakis, Kostas Stefanidis: Fairness in Group Recommendations in the Health Domain. ICDE 2017: 1481-1488 (2017). 97. Erich Teppan, Marcus Zanker, M: Decision biases in recommender systems. Journal of Internet Commerce, 14(2), 255-275 (2015). 98. Mucun Tian, Michael D. Ekstrand: Estimating Error and Bias in Offline Evaluation Results. CHIIR 2020: 392-396 (2020). 132 Advances in Bias-aware Recommendation on the Web Boratto and Marras
  133. 133. References #11 99. Songül Tolan: Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges. CoRR abs/1901.04730 (2019). 100. Raciel Yera Toledo, Yailé Caballero Mota, Luis Martínez-López: Correcting noisy ratings in collaborative recommender systems. Knowl. Based Syst. 76: 96-108 (2015). 101. Andreu Vall, Massimo Quadrana, Markus Schedl, Gerhard Widmer: Order, context and popularity bias in next-song recommendations. Int. J. Multim. Inf. Retr. 8(2): 101-113 (2019). 102. Mengting Wan, Jianmo Ni, Rishabh Misra, Julian J. McAuley: Addressing Marketing Bias in Product Recommendations. WSDM 2020: 618-626 (2020). 103. Xiaojie Wang, Rui Zhang, Yu Sun, Jianzhong Qi: Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random. ICML 2019: 6638-6647 (2019). 104. Jacek Wasilewski, Neil Hurley: Are You Reaching Your Audience?: Exploring Item Exposure over Consumer Segments in Recommender Systems. UMAP 2018: 213-217 (2018). 105. Bin Xia, Junjie Yin, Jian Xu, Yun Li: WE-Rec: A fairness-aware reciprocal recommendation based on Walrasian equilibrium. Knowl. Based Syst. 182 (2019). 106. Yuanbo Xu, Yongjian Yang, Jiayu Han, En Wang, Fuzhen Zhuang, Hui Xiong: Exploiting the Sentimental Bias between Ratings and Reviews for Enhancing Recommendation. ICDM 2018: 1356-1361 (2018). 107. Himank Yadav, Zhengxiao Du, Thorsten Joachims: Fair Learning-to-Rank from Implicit Feedback. CoRR abs/1911.08054 (2019). 108. Longqi Yang, Yin Cui, Yuan Xuan, Chenyang Wang, Serge J. Belongie, Deborah Estrin: Unbiased offline recommender evaluation for missing-not-at-random implicit feedback. RecSys 2018: 279-287 (2018). 133 Advances in Bias-aware Recommendation on the Web Boratto and Marras
  134. 134. References #12 110. Ke Yang, Julia Stoyanovich: Measuring Fairness in Ranked Outputs. SSDBM 2017: 22:1-22:6 (2017). 111. Sirui Yao, Bert Huang: Beyond Parity: Fairness Objectives for Collaborative Filtering. NIPS 2017: 2921-2930 (2017). 112. Xinyang Yi, Ji Yang, Lichan Hong, Derek Zhiyuan Cheng, Lukasz Heldt, Aditee Kumthekar, Zhe Zhao, Li Wei, Ed H. Chi: Sampling-bias-corrected neural modeling for large corpus item recommendations. RecSys 2019: 269-277 (2019). 113. Meike Zehlike, Carlos Castillo: Reducing Disparate Exposure in Ranking: A Learning To Rank Approach. WWW 2020: 2849-2855 (2020). 114. Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, Ricardo Baeza-Yates: FA*IR: A Fair Top-k Ranking Algorithm. CIKM 2017: 1569-1578 (2017). 115. Shuai Zhang, Lina Yao, Aixin Sun, Yi Tay: Deep Learning Based Recommender System: A Survey and New Perspectives. ACM Comput. Surv. 52(1): 5:1-5:38 (2019) 116. Xiaoying Zhang, Hong Xie, Junzhou Zhao, John C. S. Lui: Modeling the Assimilation-Contrast Effects in Online Product Rating Systems: Debiasing and Recommendations. IJCAI 2018: 5409-5413 (2018). 117. Xing Zhao, Ziwei Zhu, Majid Alfifi, James Caverlee: Addressing the Target Customer Distortion Problem in Recommender Systems. WWW 2020: 2969-2975 (2020). 118. Yong Zheng, Tanaya Dave, Neha Mishra, Harshit Kumar: Fairness In Reciprocal Recommendations: A Speed-Dating Study. UMAP 2018: 29-34 (2018). 119. Ziwei Zhu, Xia Hu, James Caverlee: Fairness-Aware Tensor-Based Recommendation. CIKM 2018: 1153-1162 (2018). 120. Ziwei Zhu, Jianling Wang, and James Caverlee. 2020. Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '20). 134 Advances in Bias-aware Recommendation on the Web Boratto and Marras
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WSDM 2021 Tutorial on Advances in Bias-aware Recommendation on the Web

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