Recommendation systems, also known as recommendation engines, are a type of information system whose purpose is to suggest, or recommend items or actions to users.
The recommendations may consist of:
-> retail items (movies, books, etc.) or
-> actions, such as following other users in a social network.
It can be said that, Recommendation engines are nothing but an automated form of a “shop counter guy”. You ask him for a product. Not only he shows that product, but also the related ones which you could buy. They are well trained in cross selling and up selling. So, does our recommendation engines.
Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning models.
Amazon, YouTube, Netflix, Facebook and many other companies generate an important fraction of their revenues thanks to their ability to model and accurately predict users ratings and preferences.
In this presentation we cover the following points:
→ introduction to recommender systems
→ working with explicit vs implicit feedback
→ content-based vs collaborative filtering approaches
→ user-based and item-item methods
→ machine learning and deep learning models
→ pros & cons of the methods: scalability, accuracy, explainability
In this lecture, I will first cover the recent advances in neural recommender systems such as autoencoder-based and MLP-based recommender systems. Then, I will introduce the recent achievement for automatic playlist continuation in music recommendation.
Recommendation systems, also known as recommendation engines, are a type of information system whose purpose is to suggest, or recommend items or actions to users.
The recommendations may consist of:
-> retail items (movies, books, etc.) or
-> actions, such as following other users in a social network.
It can be said that, Recommendation engines are nothing but an automated form of a “shop counter guy”. You ask him for a product. Not only he shows that product, but also the related ones which you could buy. They are well trained in cross selling and up selling. So, does our recommendation engines.
Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning models.
Amazon, YouTube, Netflix, Facebook and many other companies generate an important fraction of their revenues thanks to their ability to model and accurately predict users ratings and preferences.
In this presentation we cover the following points:
→ introduction to recommender systems
→ working with explicit vs implicit feedback
→ content-based vs collaborative filtering approaches
→ user-based and item-item methods
→ machine learning and deep learning models
→ pros & cons of the methods: scalability, accuracy, explainability
In this lecture, I will first cover the recent advances in neural recommender systems such as autoencoder-based and MLP-based recommender systems. Then, I will introduce the recent achievement for automatic playlist continuation in music recommendation.
Overview of the Recommender system or recommendation system. RFM Concepts in brief. Collaborative Filtering in Item and User based. Content-based Recommendation also described.Product Association Recommender System. Stereotype Recommendation described with advantage and limitations.Customer Lifetime. Recommender System Analysis and Solving Cycle.
Past, present, and future of Recommender Systems: an industry perspectiveXavier Amatriain
Keynote for the ACM Intelligent User Interface conference in 2016 in Sonoma, CA. I start with the past by talking about the Recommender Problem, and the Netflix Prize. Then I go into the Present and the Future by talking about approaches that go beyond rating prediction and ranking and by finishing with some of the most important lessons learned over the years. Throughout my talk I put special emphasis on the relation between algorithms and the User Interface.
Recommendation Systems: Applying Amazon's Collaborative Filtering Methods to ...Nguyen Cao
An overview about design, implementation & optimization of recommendation services for e-commerce by researching about how Amazon delivers its recommendation systems
Anatomy of an eCommerce Search Engine by Mayur DatarNaresh Jain
In this talk, the chief Data scientist of Flipkart will uncover the various challenges in running an e-commerce search platform like scale, recency, update rates, business shaping etc. He will also explain the overall system architecture of the search platform and get into the details of some of the sub-systems, including the query understanding and rewriting sub-system.
Collaborative Recommender System for Music using Pytorch. Combine Matrix Factorization and Neural Networks for improved performance. Python sample code included.
AI-driven product innovation: from Recommender Systems to COVID-19Xavier Amatriain
AI/Machine Learning has become an integral part of many household tech products, from Netflix to our phones. In this talk I will draw from my experience driving AI teams at some of those companies to showcase how AI can positively impact products as different as Netflix and Curai, an online telehealth service.
Recommender Systems from A to Z – Model TrainingCrossing Minds
This second meetup will be about training different models for our recommender system. We will review the simple models we can build as a baseline. After that, we will present the recommender system as an optimization problem and discuss different training losses. We will mention linear models and matrix factorization techniques. We will end the presentation with a simple introduction to non-linear models and deep learning.
Presentation outlining the UnBias project, an EPSRC funded project about transparency of biases in algorithm behaviour, often due to unavoidable implicit choices that had to be made.
This presentation was given at the DASTS16 conference in Aarhus Denmark on June 3rd 2016.
Overview of the Recommender system or recommendation system. RFM Concepts in brief. Collaborative Filtering in Item and User based. Content-based Recommendation also described.Product Association Recommender System. Stereotype Recommendation described with advantage and limitations.Customer Lifetime. Recommender System Analysis and Solving Cycle.
Past, present, and future of Recommender Systems: an industry perspectiveXavier Amatriain
Keynote for the ACM Intelligent User Interface conference in 2016 in Sonoma, CA. I start with the past by talking about the Recommender Problem, and the Netflix Prize. Then I go into the Present and the Future by talking about approaches that go beyond rating prediction and ranking and by finishing with some of the most important lessons learned over the years. Throughout my talk I put special emphasis on the relation between algorithms and the User Interface.
Recommendation Systems: Applying Amazon's Collaborative Filtering Methods to ...Nguyen Cao
An overview about design, implementation & optimization of recommendation services for e-commerce by researching about how Amazon delivers its recommendation systems
Anatomy of an eCommerce Search Engine by Mayur DatarNaresh Jain
In this talk, the chief Data scientist of Flipkart will uncover the various challenges in running an e-commerce search platform like scale, recency, update rates, business shaping etc. He will also explain the overall system architecture of the search platform and get into the details of some of the sub-systems, including the query understanding and rewriting sub-system.
Collaborative Recommender System for Music using Pytorch. Combine Matrix Factorization and Neural Networks for improved performance. Python sample code included.
AI-driven product innovation: from Recommender Systems to COVID-19Xavier Amatriain
AI/Machine Learning has become an integral part of many household tech products, from Netflix to our phones. In this talk I will draw from my experience driving AI teams at some of those companies to showcase how AI can positively impact products as different as Netflix and Curai, an online telehealth service.
Recommender Systems from A to Z – Model TrainingCrossing Minds
This second meetup will be about training different models for our recommender system. We will review the simple models we can build as a baseline. After that, we will present the recommender system as an optimization problem and discuss different training losses. We will mention linear models and matrix factorization techniques. We will end the presentation with a simple introduction to non-linear models and deep learning.
Presentation outlining the UnBias project, an EPSRC funded project about transparency of biases in algorithm behaviour, often due to unavoidable implicit choices that had to be made.
This presentation was given at the DASTS16 conference in Aarhus Denmark on June 3rd 2016.
Machine learning based recommender system for e-commerceIAESIJAI
Nowadays, e-commerce is becoming an essential part of business for many reasons, including the simplicity, availability, richness and diversity of products and services, flexibility of payment methods and the convenience of shopping remotely without losing time. These benefits have greatly optimized the lives of users, especially with the technological development of mobile devices and the availability of the Internet anytime and anywhere. Because of their direct impact on the revenue of e-commerce companies, recommender systems are considered a must in this field. Recommender systems detect items that match the customer's needs based on the customer's previous actions and make them appear in an interesting way. Such a customized experience helps to increase customer engagement and purchase rates as the suggested items are tailored to the customer's interests. Therefore, perfecting recommendation systems that allow for more personalized and accurate item recommendations is a major challenge in the e-marketing world. In our study, we succeeded in developing an algorithm to suggest personal recommendations to customers using association rules via the Frequent-Pattern-Growth algorithm. Our technique generated good results with a high average probability of purchasing the next product suggested by the recommendation system.
Presentation at the AoIR2017 conference at Tartu, Estonia summarizing preliminary results from workshops by the EPSRC funded UnBias project (http://unbias.wp.horizon.ac.uk/)
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
Recommendation System Using Social Networking ijcseit
With the proliferation of electronic commerce and knowledge economy environment both organizations and
individuals generate and consume a large amount of online information. With the huge availability of
product information on website, many times it becomes difficult for a consumer to locate item he wants to
buy. Recommendation Systems [RS] provide a solution to this. Many websites such as YouTube, e-Bay,
Amazon have come up with their own versions of Recommendation Systems. However Issues like lack of
data, changing data, changing user preferences and unpredictable items are faced by these
recommendation systems. In this paper we propose a model of Recommendation systems in e-commerce
domain which will address issues of cold start problem and change in user preference problem. Our work
proposes a novel recommendation system which incorporates user profile parameters obtained from Social
Networking website. Our proposed model SNetRS is a collaborative filtering based algorithm, which
focuses on user preferences obtained from FaceBook. We have taken domain of books to illustrate our
model.
An Efficient Content, Collaborative – Based and Hybrid Approach for Movie Rec...ijtsrd
The purpose of the project is to research about Content and Collaborative based movie recommendation engines. Nowadays recommender systems are used in our day to day life. We try to understand the distinct types of reference engines systems and compare their work on the movies datasets. We start to produce a versatile model to complete this study and start by developing and relating the different kinds of prototypes on a minor dataset of 100,000 evaluations. The growth of e commerce has given rise to recommendation engines. Several recommendation engines exist within the market to recommend a wide variety of goods to users. These recommendations support various aspects such as users interests, users history, users locations, and more. Away from all the above aspects one thing is common which is individuality. Content and collaborative based movie recommendation engines recommend users based on the users viewpoint, whereas many things are there within the marketplace that are related to which a user is uninformed of. This stuff should also be suggested by the engine to clients But due to the range of individuality , these machines do not suggest things that are out of the crate. The Hybrid System of Movie Recommendation Engine has crossed this variety of individuality. The Movie Recommendation Engine will suggest movies to clients according to their interest and be evaluated by other clients who are almost user like. Additionally, for this, there are web services that are capable of acting as a tool adornment. Rajeev Kumar | Guru Basava | Felicita Furtado "An Efficient Content, Collaborative – Based and Hybrid Approach for Movie Recommendation Engine" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30737.pdf Paper Url :https://www.ijtsrd.com/engineering/information-technology/30737/an-efficient-content-collaborative-%E2%80%93-based-and-hybrid-approach-for-movie-recommendation-engine/rajeev-kumar
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/09/responsible-ai-tools-and-frameworks-for-developing-ai-solutions-a-presentation-from-intel/
Mrinal Karvir, Senior Cloud Software Engineering Manager at Intel, presents the “Responsible AI: Tools and Frameworks for Developing AI Solutions” tutorial at the May 2023 Embedded Vision Summit.
Over 90% of businesses using AI say trustworthy and explainable AI is critical to business, according to Morning Consult’s IBM Global AI Adoption Index 2021. If not designed with responsible considerations of fairness, transparency, preserving privacy, safety and security, AI systems can cause significant harm to people and society and result in financial and reputational damage for companies.
How can we take a human-centric approach to design AI solutions? How can we identify different types of bias and what tools can we use to mitigate those? What are model cards, and how can we use them to improve transparency? What tools can we use to preserve privacy and improve security? In this talk, Karvir discusses practical approaches to adoption of responsible AI principles. She highlights relevant tools and frameworks and explores industry case studies. She also discusses building a well-defined response plan to help address an AI incident efficiently.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
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At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
2. Key Takeaways
Fairness in Machine Learning
Need for Fairness in ML based Recommendation
Systems
Fairness-aware Machine Learning Best
Practices
Multi-sided Fairness , Platforms and Metrics
3. Why Fairness in Recommendations
Fair Housing Act LGBT
Fairness Act
Disability
Status
Disparate
Impact
Source - https://www.nytimes.com/2019/05/07/opinion/google-sundar-pichai-privacy.html
Disability
Laws and Policies
4. Ethical Artificial Intelligence – Fairness Origin
Professor Klaus Schwab - Executive Chairman of WORLD ECONOMIC
FORUM
“We must address, individually
and collectively, moral and
ethical issues raised by cutting-
edge research in artificial
intelligence and biotechnology,
which will enable significant life
extension, designer babies, and
memory extraction.” —Klaus
Schwab
Source - https://www.statista.com/chart/18805/highest-penalties-in-privacy-enforcement-actions-worldwide/
5. Foundation of Algorithmic Justice League
Joy Buolamwini - computer scientist and digital activist based at the
MIT Media Lab
Whether AI will help us reach our aspirations or
reinforce the unjust inequalities is ultimately up to us.
If we fail to make ethical and inclusive artificial
intelligence, we risk losing gains made in civil rights
and gender equity under the guise of machine
neutrality
Source - https://www.statista.com/chart/18805/highest-penalties-in-privacy-enforcement-actions-worldwide/
6. AI Regulation
Sundar Pichai
The head of Google and parent company
Alphabet has called for artificial
intelligence (AI) to be regulated
● Fair Marketplace
● Legal obligation
● Social Responsibility
● Business Requirement /Model
Source - https://builtin.com/artificial-intelligence/ai-laws-regulations
7. Unfair Recommendation Systems from Biases
Source - Simple Demographics Often Identify People Uniquely
Differences in demographics or other user characteristics.
Differences across online and offline communities, platforms
and contexts
Lexical, syntactic, semantic, and structural differences in the contents
Connections, interactions, or activities obtained from networks and their attributes
Society norms, prejudices, economical status
Seasonal, weekly or observed at a certain time
Population
Behavioral
Content
Production
Linking
Social/
Economic
Temporal
Types of Biases
8. Recommendation Systems
Differences in demographics or other user characteristics.
Differences across online and offline communities, platforms
and contexts
Lexical, syntactic, semantic, and structural differences in the contents
Connections, interactions, or activities obtained from networks and their attributes
Society norms, prejudices, economical status
Seasonal, weekly or observed at a certain time
Impact of Bias
● Biased customer reviews
● Disparate impact on minority drivers
● Unjust outcomes with low wages
9. Recommend Fairly to All Groups of Users
Fairness
Escalate
Source -https://course.ece.cmu.edu/~ece734/lectures/lecture-2018-10-08-deanonymization.pdf
Product Goals Stakeholder Identification
Analyze
Mitigate TransparencyMonitor Performance
Best Practices for Removing Bias
11. Where and How
Social Science
Backgrounds
Gender and sexual
orientation
Diverse Identities - Race, Nationality, Religion
Google’s Responsible AI - Diversity and Inclusion
12. Fairness for Individual and Groups
Pre-processing , Learning Function , Post Processing
Source - https://arxiv.org/pdf/1906.08732.pdf
FAIRNESS COMMON TERMS
Source
: https://arxiv.org/pdf/1906.08732.pdf
Envy-freeness requires
that every user should
prefer their own
allocation to that of
everyone else; it ignores
users’ qualifications and
considers preferences
Individual or metric
fairness ignores
preferences and
requires that similar
users should be
treated similarly.
Multiple task
fairness, requires
that individual
fairness is satisfied
separately and
simultaneously for
all categories
Inter-category envy-
freeness, which allows
users to specify a set of
categories that they
“care” about, and
guarantees that they
receive at least that they
care about as any other
individual
13. Fairness Constraints with 2 latent variables:
ProtectedItemRating(i) ⇒ UnProtectedItemRating(i)
UnProtectedItemRating(i) ⇒ ProtectedItemRating(i)
Protected(u) ∧ RATING(u, i) ∧ ItemGroup(i, g)
⇒ ProtectedItemGroupRating(g)
¬ Protected(u) ∧ Rating(u, i) ∧ ItemGroup(i, g)
⇒ UnProtectedItemGroupRating(g)
Recommendation Systems
MetricsSL
No
1.
2.
3.
4.
Statistical independence between recommendation
results and the sensitive attribute
Value unfairness (estimation error across user groups)
Absolute unfairness
Underestimation unfairness
5. Overestimation unfairness
Fairness Metrics between advantaged and disadvantaged groups
6. Non-parity Fairness
Source -https://arxiv.org/pdf/1809.09030.pdf
𝑝𝑖- d-dimensional vector representing the ith user,
𝑞 𝑗 − d−dimensional vector representing the jth item,
𝑢𝑖 𝑎𝑛𝑑 𝑣𝑗. user and item respectively
X – observed ratings
14. Recommendation Diversity and User Fairness
Ranking algorithms
Top-l CF
recommendations
Sample K items
uniformly – K nearest
neighbors
Greedy algo – New
recommendations above
threshold
● Individual diversity - Diverse recommendations
to the users.
● Aggregate diversity - Improve item diversity by
recommending them at least once across all
users.
● Limitations – No fairness like differential
treatment of two users or two items.
● Impact – Disparity among users increases with
Aggregate Diversity
CF Output
Source - https://www.researchgate.net/publication/324640535_User_Fairness_in_Recommender_Systems
15. Different Types of multi-sided Fairness
● Multi-stakeholder Recommender Systems with multiple
goals (e.g. LinkedIn, Etsy)
● Reciprocal Recommendations –Bilateral and acceptable
to both parties. (e.g. job, mentor, business partner)
○ Peer-to-peer recommendation –Sharing economy,
online advertising and scientific collaboration
● Objective – Maximize system utility
Multi-sided Fairness in Recommendation Systems
Source - http://proceedings.mlr.press/v81/burke18a/burke18a.pdf
16. Regularization based Sparse Linear Method (SLIM) with Neighborhood balance
● Provider Fairness – Market Diversity , avoid
monopoly by recommending minority owned
businesses.
● Consumer Fairness – Personalization, Disparate
impact of recommendation on protected
classes
Multi-sided Fairness – User Based Neighborhoods
Source -http://proceedings.mlr.press/v81/burke18a/burke18a.pdf
Unbalanced Neighbors
Balanced Neighbors
FairnessFairness
● Regression coefficient <user, item> pair
● Minimize regularized loss function
17. Multi-sided Fairness - Item Based Neighborhoods
Source -https://medium.com/@cfpinela/recommender-systems-user-based-and-item-based-collaborative-filtering-
5d5f375a127f
● Use case - Exposure to loans
from different geographic
regions
● Items in protected group are
in neighborhoods that have
balanced membership of
items from the unprotected
group● Balance between protected and non-
protected neighbors for each user.
Balanced Neighborhood SLIM – Balance Personalization with Fairness
18. Two-sided Fairness Two-sided Platforms
● Fair recommendation -> Fair Allocation
○ Maximin Share (MMS) of exposure for Producers
and Envy-Free up to One Good (EF1) fairness for
every customer
● Cardinality constrained fair allocation
○ All items are grouped into disjoint categories and no
agent receives more than a pre-specified number of
items from same category
○ Exactly k items are allocated to each customer
Datasets – GL-CUSTOM, (Relevance
Scoring) , GL-FACT and LAST.FM DATASET
–(Latent Factorization)
Source - https://people.mpi-sws.org/~achakrab/papers/patro_FairRec_WWW20.pdf
19. Source - https://people.mpi-sws.org/~achakrab/papers/patro_FairRec_WWW20.pdf
FAIR RECOMMENDATION SYSTEMSAdvantages and Metrics
MetricsSL
No
1.
2.
3.
4.
Fraction of Satisfied Producers
Inequality of Product Exposures
Exposure Loss on Producers
Mean-Average Envy
5. Loss and Disparity in Customer Utilities
● Advantages
○ Economic
○ Social
○ Judicial and
○ Long-term sustainability
K items
20. Evaluate Fair Recommender Systems - Pairwise
Comparisons Original vs Pairwise Regularization
● Pairwise regularization to optimize for
inter-group pairwise fairness.
● Corresponds to ranking performance
● Pairwise Fairness - Likelihood of a clicked
item being ranked above another relevant
unclicked item is the same across both
groups (same/opposite), conditioned on
the items have safe level of engagement.
● Real world scalable product grade systems
Source - http://alexbeutel.com/papers/kdd2019_pairwise_fairness.pdf
Intra Group and Inter Group Pairwise Fairness
22. Conclusion
● Handle sparse data
● Bias from Advertisers
● Formulate right personalization and key
outcome
● Techniques to mitigate harms and mis-
behaviors
● Computational enhancement for Multi-
stakeholder fairness optimization
● Network structures that define relationships
between providers and users
● Sequential notions of fairness into recommender
systems with additional time-dependent
constraints
● Equal treatment vs equal outcome
Source: https://www.researchgate.net/publication/314971193_Optimal_Performance_vs_Fairness_Tradeoff_for_Resource_Allocation_in_Wireless_Systems
