This document discusses using multiple objectives in collaborative filtering recommender systems. It proposes a framework that optimizes for an accuracy baseline objective while also considering additional user and system objectives. Specifically, it explores promoting less popular, "long tail" items from a user perspective, and incorporating item availability constraints from a system perspective to reduce waiting times. Experiments show the approach can improve these other objectives with only minor losses to recommendation accuracy. The framework provides a flexible way to optimize collaborative filtering for multiple goals.
Predictive Modeling: Predict Premium Subscriber for a Leading International M...Kaushik Nuvvula
The motive of this project is to build a best performing model to predict which current non-subscribers would be more likely to convert to premium subscribers (paid subscription).
• Built and evaluated each model with training and validation data. Pre-processed data with oversampling, normalization and parameter optimization techniques
• Used a majority voting based ensemble of K Nearest Neighbors (k-NN), Support Vector Machines (SVM) and Neural Networks to develop the best cost-effective model
• Applied feature selection, and oversampling to improve model accuracy, and used ensemble cost effective techniques to trade off accuracy for lower misclassification cost
Recommender systems using collaborative filteringD Yogendra Rao
This document summarizes a student project on implementing recommender systems. The project objectives were to design a website using user-based, item-based, and model-based collaborative filtering as well as MapReduce to generate movie recommendations. The system was tested on the MovieLens dataset using MAE and RMSE metrics, with user-based filtering found to have the best performance. The document outlines the technical aspects of the recommendation system including the technologies used, website architecture, and references.
[AFEL] Neighborhood Troubles: On the Value of User Pre-Filtering To Speed Up ...Emanuel Lacić
In this paper, we present work-in-progress on applying user pre-filtering to speed up and enhance recommendations based on Collaborative Filtering. We propose to pre-filter users in order to extracta smaller set of candidate neighbors, who exhibit a high number of overlapping entities and to compute the final user similarities based on this set. To realize this, we exploit features of the high-performance search engine Apache Solr and integrate them into a scalable recommender system. We have evaluated our approach on a dataset gathered from Foursquare and our evaluation results suggest that our proposed user pre-filtering step can help to achieve both a better runtime performance as well as an increase in overall recommendation accuracy.
Comparative Study of Machine Learning Algorithms for Sentiment Analysis with ...Sagar Deogirkar
Comparing the State-of-the-Art Deep Learning with Machine Learning algorithms performance on TF-IDF vector creation for Sentiment Analysis using Airline Tweeter Data Set.
There are several challenges in evaluating information retrieval systems, including the subjectivity and dynamic nature of relevancy judgments. Common evaluation metrics include precision, recall, F-measure, mean average precision, discounted cumulative gain, and normalized discounted cumulative gain. These metrics are calculated using test collections consisting of queries, relevant documents, and system rankings to measure how closely systems can match human relevance assessments at different levels of a ranked list.
A Flexible Recommendation System for Cable TVFrancisco Couto
1. The document proposes a flexible recommendation system for cable TV to address issues like information overflow and dissatisfaction from users.
2. It describes extracting implicit feedback from users and engineering contextual features to create a large-scale dataset for learning recommendations.
3. An evaluation of the recommendation system shows that a learning to rank approach with contextual information outperforms other methods in accuracy while maintaining diversity and novelty, though recommending new programs requires more investigation.
The document discusses using singular value decomposition (SVD) for collaborative filtering recommender systems on big data. It presents experiments applying SVD to a movie rating dataset using Apache Hadoop and Spark. The experiments analyze the effect of varying parameters like number of dimensions, training ratio, and imputation techniques on prediction accuracy measured by mean absolute error. The results show SVD achieves comparable accuracy to previous work and is effective for big data when choosing right parameters and frameworks like Hadoop and Spark. Future work is proposed to improve the system through techniques like incremental SVD and deploying on a cluster.
Predictive Modeling: Predict Premium Subscriber for a Leading International M...Kaushik Nuvvula
The motive of this project is to build a best performing model to predict which current non-subscribers would be more likely to convert to premium subscribers (paid subscription).
• Built and evaluated each model with training and validation data. Pre-processed data with oversampling, normalization and parameter optimization techniques
• Used a majority voting based ensemble of K Nearest Neighbors (k-NN), Support Vector Machines (SVM) and Neural Networks to develop the best cost-effective model
• Applied feature selection, and oversampling to improve model accuracy, and used ensemble cost effective techniques to trade off accuracy for lower misclassification cost
Recommender systems using collaborative filteringD Yogendra Rao
This document summarizes a student project on implementing recommender systems. The project objectives were to design a website using user-based, item-based, and model-based collaborative filtering as well as MapReduce to generate movie recommendations. The system was tested on the MovieLens dataset using MAE and RMSE metrics, with user-based filtering found to have the best performance. The document outlines the technical aspects of the recommendation system including the technologies used, website architecture, and references.
[AFEL] Neighborhood Troubles: On the Value of User Pre-Filtering To Speed Up ...Emanuel Lacić
In this paper, we present work-in-progress on applying user pre-filtering to speed up and enhance recommendations based on Collaborative Filtering. We propose to pre-filter users in order to extracta smaller set of candidate neighbors, who exhibit a high number of overlapping entities and to compute the final user similarities based on this set. To realize this, we exploit features of the high-performance search engine Apache Solr and integrate them into a scalable recommender system. We have evaluated our approach on a dataset gathered from Foursquare and our evaluation results suggest that our proposed user pre-filtering step can help to achieve both a better runtime performance as well as an increase in overall recommendation accuracy.
Comparative Study of Machine Learning Algorithms for Sentiment Analysis with ...Sagar Deogirkar
Comparing the State-of-the-Art Deep Learning with Machine Learning algorithms performance on TF-IDF vector creation for Sentiment Analysis using Airline Tweeter Data Set.
There are several challenges in evaluating information retrieval systems, including the subjectivity and dynamic nature of relevancy judgments. Common evaluation metrics include precision, recall, F-measure, mean average precision, discounted cumulative gain, and normalized discounted cumulative gain. These metrics are calculated using test collections consisting of queries, relevant documents, and system rankings to measure how closely systems can match human relevance assessments at different levels of a ranked list.
A Flexible Recommendation System for Cable TVFrancisco Couto
1. The document proposes a flexible recommendation system for cable TV to address issues like information overflow and dissatisfaction from users.
2. It describes extracting implicit feedback from users and engineering contextual features to create a large-scale dataset for learning recommendations.
3. An evaluation of the recommendation system shows that a learning to rank approach with contextual information outperforms other methods in accuracy while maintaining diversity and novelty, though recommending new programs requires more investigation.
The document discusses using singular value decomposition (SVD) for collaborative filtering recommender systems on big data. It presents experiments applying SVD to a movie rating dataset using Apache Hadoop and Spark. The experiments analyze the effect of varying parameters like number of dimensions, training ratio, and imputation techniques on prediction accuracy measured by mean absolute error. The results show SVD achieves comparable accuracy to previous work and is effective for big data when choosing right parameters and frameworks like Hadoop and Spark. Future work is proposed to improve the system through techniques like incremental SVD and deploying on a cluster.
Quality andc apability hand out 091123200010 Phpapp01jasonhian
The document outlines key concepts in quality management and Six Sigma methodology. It discusses definitions of quality, total quality management (TQM), and Six Sigma. Six Sigma aims to reduce defects through eliminating variation and achieving near zero defect levels. It uses a Define-Measure-Analyze-Improve-Control (DMAIC) methodology. Statistical process control charts and process capability indices are also introduced to measure quality performance. An example of Mumbai's successful lunch delivery system achieving over 5-sigma quality levels is provided.
Bridging the Gap: Machine Learning for Ubiquitous Computing -- EvaluationThomas Ploetz
Tutorial @Ubicomp 2015: Bridging the Gap -- Machine Learning for Ubiquitous Computing (evaluation session).
A tutorial on promises and pitfalls of Machine Learning for Ubicomp (and Human Computer Interaction). From Practitioners for Practitioners.
Presenter: Nils Hammerla <n.hammerla@gmail.com>
video recording of talks as they wer held at Ubicomp:
https://youtu.be/LgnnlqOIXJc?list=PLh96aGaacSgXw0MyktFqmgijLHN-aQvdq
- Six Sigma is a quality methodology that aims for near perfection with 3.4 defects per million opportunities. It was developed by Motorola in 1987.
- Key concepts include process capability index (Cp), process variation, and specification limits. A Cp of 2.0 or higher is needed to achieve Six Sigma quality.
- The DMAIC methodology is used for improving existing processes and focuses on defining problems, measuring processes, analyzing causes, improving processes, and controlling future performance. DFSS designs new processes at Six Sigma quality levels using approaches like DMADV.
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The document discusses the Catalan government's efforts to improve quality governance and problem resolution for its information and communication technology (ICT) systems. It oversaw the centralization of ICT budget and management through CTTI to increase efficiency. CTTI implemented a new ICT model and quality governance framework to comply with goals of cost reduction, standardization, and externalization. This included classifying applications by risk to tailor quality activities, implementing tools for testing and monitoring, and creating task forces to diagnose and resolve difficult problems by bringing together developers, quality analysts, and operations teams. Continuous improvement efforts focused on learning from experience and balancing quality activities according to risk.
AWS re:Invent 2016: Getting to Ground Truth with Amazon Mechanical Turk (MAC201)Amazon Web Services
Jump-start your machine learning project by using the crowd to build your training set. Before you can train your machine learning algorithm, you need to take your raw inputs and label, annotate, or tag them to build your ground truth. Learn how to use the Amazon Mechanical Turk marketplace to perform these tasks. We share Amazon's best practices, developed while training our own machine learning algorithms, and walk you through quickly getting affordable and high-quality training data.
Performance doesn’t have the same definition between system administrators, developpers and business teams. What is Performance ? High CPU usage, not scalable web site, low business transaction rate per sec, slow response time, … This presentation is about maths, code performance, load testing, web performance, best practices, … Working on performance optimizaton is a very broad topic. It’s important to really understand main concepts and to have a clean and strong methodology because it could be a very time consumming activity. Happy reading !
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Automated Parameterization of Performance Models from MeasurementsWeikun Wang
This is a tutorial presented in ICPE 2016 (https://icpe2016.spec.org/). In this tutorial, we present the problem of estimating parameters of performance models from measurements of real systems and discuss algorithms that can support researchers and practitioners in this task. The focus lies on performance models based on queueing systems, where the estimation of request arrival rates and service demands is a required input to the model. In the tutorial, we review existing estimation methods for service demands and present models to characterize time-varying arrival processes. The tutorial also demonstrates the use of relevant tools that automate demand estimation, such as LibRede, FG and M3A.
A Top-N Recommender System Evaluation Protocol Inspired by Deployed SystemsAlan Said
he evaluation of recommender systems is crucial for their development. In today's recommendation landscape there are many standardized recommendation algorithms and approaches, however, there exists no standardized method for experimental setup of evaluation -- not even for widely used measures such as precision and root-mean-squared error. This creates a setting where comparison of recommendation results using the same datasets becomes problematic. In this paper, we propose an evaluation protocol specifically developed with the recommendation use-case in mind, i.e. the recommendation of one or several items to an end user. The protocol attempts to closely mimic a scenario of a deployed (production) recommendation system, taking specific user aspects into consideration and allowing a comparison of small and large scale recommendation systems. The protocol is evaluated on common recommendation datasets and compared to traditional recommendation settings found in research literature. Our results show that the proposed model can better capture the quality of a recommender system than traditional evaluation does, and is not affected by characteristics of the data (e.g. size. sparsity, etc.).
The document summarizes an Analytics Vidhya meetup event. It discusses that the meetups will occur once a month, with the next one on May 24th. It aims to provide networking and learning around data science, big data, machine learning and IoT. It introduces the volunteer organizers and outlines the agenda, which includes an introduction, discussing the model building lifecycle, data exploration techniques, and modeling techniques like logistic regression, decision trees, random forests, and SVMs. It provides details on practicing these techniques by predicting survival on the Titanic dataset.
The document provides an overview of Daniel Austin's Web Performance Boot Camp. The class aims to (1) provide an understanding of web performance, (2) empower attendees to identify and resolve performance issues, and (3) demonstrate common performance tools. The class covers topics such as the impact of performance on business, definitions of performance, statistical analysis, queuing theory, the OSI model, and the MPPC model for analyzing the multiple components that determine overall web performance. Attendees will learn tools like Excel, web testing tools, browser debugging tools, and optional tools like R and Mathematica.
When a FILTER makes the difference in continuously answering SPARQL queries ...Shima Zahmatkesh
This document proposes new maintenance policies for refreshing a local replica used to continuously evaluate SPARQL queries over streaming and background linked data when the queries contain a FILTER clause. It introduces the problem, motivates the research question, and hypothesizes that policies focusing on mappings close to the filter threshold will keep the replica fresher. Experimental results show the proposed "Filter Update Policy" outperforms existing policies from state-of-the-art approaches when selectivity is over 60%, and combining it with other policies performs even better. Future work is outlined to broaden the class of supported queries.
Big Data Day LA 2016/ Use Case Driven track - Shaping the Role of Data Scienc...Data Con LA
At IRIS.TV, our business builds algorithmic solutions for video recommendation with the end goal to deliver a great user experience as evidenced by users viewing more video content. This talk outlines our reasons for expanding from a descriptive/predictive approach to data analytics toward a philosophy that features more prescriptive analytics, driven by our data science team.
Argumentation in Artificial Intelligence: From Theory to Practice (Practice)Mauro Vallati
Part on Practice of the IJCAI 2017 Tutorial titled "Argumentation in Artificial Intelligence: From Theory to Practice", from Federico Cerutti and Mauro Vallati
This thesis focuses on performance management techniques for cloud services. It presents work in three key areas: 1) Developing a scalable and generic resource allocation protocol for large cloud environments. 2) Building performance models to predict response times and capacity for a distributed key-value store. 3) Enabling real-time prediction of service metrics using analytics on low-level system statistics. The thesis contributes solutions for these challenging problems and identifies open questions around decentralized resource allocation, online performance management, and analytics-based forecasting at large scales.
CBM Requirements by Carl Byington - PHM Design, LLCCarl Byington
Carl Byington with PHM Design, LLC reviews:
Conceptual functional architecture:
Describes functions and functional interactions
Traces functions to capabilities or services desired in the COO
Conceptual physical architecture:
Allocates and describes the conceptual implementation of functions
Traces implementation to function
Activity Flows:
Identifies primary paths through the principal use-cases to meet the goals and interests of the stakeholders
Trades identify preferred path which, in turn, provides context for requirements derivation and operational thread development.
#phmdesign
https://phmdesign.com
Machine learning algorithm for classification of activity of daily life’sSiddharth Chakravarty
The document describes a machine learning approach to classify activities of daily living (ADL) using data from a wrist-worn accelerometer. The approach uses support vector machines (SVM) with feature engineering that includes vector magnitude and singular value decomposition. The model is trained on a dataset containing 11 ADLs performed by 16 volunteers. Hyperparameter tuning is performed to optimize the SVM, achieving up to 86% accuracy on test data when using both vector magnitude and SVD features compared to 71% accuracy using only vector magnitude. The results demonstrate an improved method for detecting ADLs but would benefit from testing on additional datasets.
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdfflufftailshop
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
Fueling AI with Great Data with Airbyte WebinarZilliz
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Quality andc apability hand out 091123200010 Phpapp01jasonhian
The document outlines key concepts in quality management and Six Sigma methodology. It discusses definitions of quality, total quality management (TQM), and Six Sigma. Six Sigma aims to reduce defects through eliminating variation and achieving near zero defect levels. It uses a Define-Measure-Analyze-Improve-Control (DMAIC) methodology. Statistical process control charts and process capability indices are also introduced to measure quality performance. An example of Mumbai's successful lunch delivery system achieving over 5-sigma quality levels is provided.
Bridging the Gap: Machine Learning for Ubiquitous Computing -- EvaluationThomas Ploetz
Tutorial @Ubicomp 2015: Bridging the Gap -- Machine Learning for Ubiquitous Computing (evaluation session).
A tutorial on promises and pitfalls of Machine Learning for Ubicomp (and Human Computer Interaction). From Practitioners for Practitioners.
Presenter: Nils Hammerla <n.hammerla@gmail.com>
video recording of talks as they wer held at Ubicomp:
https://youtu.be/LgnnlqOIXJc?list=PLh96aGaacSgXw0MyktFqmgijLHN-aQvdq
- Six Sigma is a quality methodology that aims for near perfection with 3.4 defects per million opportunities. It was developed by Motorola in 1987.
- Key concepts include process capability index (Cp), process variation, and specification limits. A Cp of 2.0 or higher is needed to achieve Six Sigma quality.
- The DMAIC methodology is used for improving existing processes and focuses on defining problems, measuring processes, analyzing causes, improving processes, and controlling future performance. DFSS designs new processes at Six Sigma quality levels using approaches like DMADV.
Customer Churn Analytics using Microsoft R OpenPoo Kuan Hoong
The document summarizes a presentation on using Microsoft R Open for customer churn analytics. It discusses using machine learning algorithms like logistic regression, support vector machines, and random forests to predict customer churn. It compares the performance of these models on a telecom customer dataset using metrics like confusion matrices and ROC curves. The presentation demonstrates building a churn prediction model in Microsoft R Open and R Tools for Visual Studio.
The document discusses the Catalan government's efforts to improve quality governance and problem resolution for its information and communication technology (ICT) systems. It oversaw the centralization of ICT budget and management through CTTI to increase efficiency. CTTI implemented a new ICT model and quality governance framework to comply with goals of cost reduction, standardization, and externalization. This included classifying applications by risk to tailor quality activities, implementing tools for testing and monitoring, and creating task forces to diagnose and resolve difficult problems by bringing together developers, quality analysts, and operations teams. Continuous improvement efforts focused on learning from experience and balancing quality activities according to risk.
AWS re:Invent 2016: Getting to Ground Truth with Amazon Mechanical Turk (MAC201)Amazon Web Services
Jump-start your machine learning project by using the crowd to build your training set. Before you can train your machine learning algorithm, you need to take your raw inputs and label, annotate, or tag them to build your ground truth. Learn how to use the Amazon Mechanical Turk marketplace to perform these tasks. We share Amazon's best practices, developed while training our own machine learning algorithms, and walk you through quickly getting affordable and high-quality training data.
Performance doesn’t have the same definition between system administrators, developpers and business teams. What is Performance ? High CPU usage, not scalable web site, low business transaction rate per sec, slow response time, … This presentation is about maths, code performance, load testing, web performance, best practices, … Working on performance optimizaton is a very broad topic. It’s important to really understand main concepts and to have a clean and strong methodology because it could be a very time consumming activity. Happy reading !
This document provides an overview of a web performance boot camp. The goals of the class are to provide an understanding of web performance, empower attendees to identify and resolve performance problems, and demonstrate common tools and techniques. The class structure includes sections on what performance is, performance basics, the MPPC model of web performance, and tools and testing. Key topics that will be covered include metrics like response time, statistical distributions, Little's Law, the response time equation, and the dimensions that impact performance like geography, network, browser/device, and page composition.
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This is a tutorial presented in ICPE 2016 (https://icpe2016.spec.org/). In this tutorial, we present the problem of estimating parameters of performance models from measurements of real systems and discuss algorithms that can support researchers and practitioners in this task. The focus lies on performance models based on queueing systems, where the estimation of request arrival rates and service demands is a required input to the model. In the tutorial, we review existing estimation methods for service demands and present models to characterize time-varying arrival processes. The tutorial also demonstrates the use of relevant tools that automate demand estimation, such as LibRede, FG and M3A.
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The document summarizes an Analytics Vidhya meetup event. It discusses that the meetups will occur once a month, with the next one on May 24th. It aims to provide networking and learning around data science, big data, machine learning and IoT. It introduces the volunteer organizers and outlines the agenda, which includes an introduction, discussing the model building lifecycle, data exploration techniques, and modeling techniques like logistic regression, decision trees, random forests, and SVMs. It provides details on practicing these techniques by predicting survival on the Titanic dataset.
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This document proposes new maintenance policies for refreshing a local replica used to continuously evaluate SPARQL queries over streaming and background linked data when the queries contain a FILTER clause. It introduces the problem, motivates the research question, and hypothesizes that policies focusing on mappings close to the filter threshold will keep the replica fresher. Experimental results show the proposed "Filter Update Policy" outperforms existing policies from state-of-the-art approaches when selectivity is over 60%, and combining it with other policies performs even better. Future work is outlined to broaden the class of supported queries.
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CBM Requirements by Carl Byington - PHM Design, LLCCarl Byington
Carl Byington with PHM Design, LLC reviews:
Conceptual functional architecture:
Describes functions and functional interactions
Traces functions to capabilities or services desired in the COO
Conceptual physical architecture:
Allocates and describes the conceptual implementation of functions
Traces implementation to function
Activity Flows:
Identifies primary paths through the principal use-cases to meet the goals and interests of the stakeholders
Trades identify preferred path which, in turn, provides context for requirements derivation and operational thread development.
#phmdesign
https://phmdesign.com
Machine learning algorithm for classification of activity of daily life’sSiddharth Chakravarty
The document describes a machine learning approach to classify activities of daily living (ADL) using data from a wrist-worn accelerometer. The approach uses support vector machines (SVM) with feature engineering that includes vector magnitude and singular value decomposition. The model is trained on a dataset containing 11 ADLs performed by 16 volunteers. Hyperparameter tuning is performed to optimize the SVM, achieving up to 86% accuracy on test data when using both vector magnitude and SVD features compared to 71% accuracy using only vector magnitude. The results demonstrate an improved method for detecting ADLs but would benefit from testing on additional datasets.
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
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Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
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Power Grid Model
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Multiple objectives in Collaborative Filtering (RecSys 2010)
1. Multiple Objectives in Collaborative Filtering
Tamas Jambor and Jun Wang
University College London
2. Structure of the talk
• Motivation
• Multiple objectives
• User perspective
– Promoting less popular items
• System perspective
– Stock management
3. Motivation
• In the RecSys community, many research efforts
are focused on recommendation accuracy
• And yet accuracy is not a only concern
• Practical recommender systems might have
multiple goals
4. Improved Accuracy != Improved User experience
Algorithm
Additional
factors
Available resources
Cost of delivery
User interface
Diverse choices
Profitability per item
Advertisement
5. Improved user experience
Available resources
Cost of delivery
User interface
Diverse choices
Profitability per item
Advertisement
Additional
factors
Accuracy
Improved
user
experience
6. Handling Multiple objectives
• Accuracy is the main objective
– Defined in the baseline algorithm
• User perspective
– Define and consider user satisfaction as priority
• System perspective
– Consider additional system related objectives
• Objectives of the system might contradict
7. Where to optimize?
• In the objective function or as a post-filter?
• Post-filters have the advantage to
– Add to any baseline algorithm
– Extend easily
– Add multiple goals
8. The proposed optimization framework
(for each user)
• Add additional constraints of w
0
11:tosubject
ˆmax
w
w
rw
T
T
w
9. Properties of the framework
• Linear optimization problem
• Recommendation as a ranking problem
• Constraints provide the means of biasing the
ranking
10. User case – Promoting the Long Tail
Current systems are biased towards popular items
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
1 2 3 4 5 6 7 8 9 10
Probabilityofanyof100mostpopularitembeingat
rankingposition
Ranking Position
SVD
User-based
Item-based
Random Sample
11. Promoting the Long Tail
• Does that reflect real user needs?
• Popular items might not be interesting for the user
• Discovering unknown item could be more valuable
• The aim is to reduce recommending popular items
– if the user is likely to be an interested in alternative
choices
– keep recommending popular items otherwise
12. Promoting the Long Tail
• Extending the optimization framework
0
11
:tosubject
ˆmax
w
w
mwm
rw
T
u
T
T
w
13. Promoting the Long Tail and Diversification
• Diversifying the results
0
11
:tosubject
ˆmax
w
w
mwm
wwrw
T
u
T
TT
w
14. Diversification
• Increase the covariance between recommended
items
– Reduce the risk of expanding the system
– Provide a wider range of choice
16. Evaluation metrics
• Recommendation as a ranking problem
• IR measures
– Normalized discounted cumulative gain (NDCG)
– Precision
– Mean reciprocal rank (MRR)
• Constraint specific measures
17. Results: Promoting the Long Tail
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
1 2 3 4 5 6 7 8 9 10
Probabilityofanyof100mostpopularitembeingat
rankingposition
Ranking Position
Baseline (SVD)
Long Tail Constraint
Long Tail Constraint and
Diversification (λ=6)
Random Sample
18. Results: Promoting the Long Tail
Baseline (SVD) LTC LTC and Div (λ=6)
NDCG@10 0.8808 0.8780 (-0.3%) 0.8715 (-1.0%)
P@10 0.8204 0.8207 (+0.2%) 0.8177 (-0.3%)
MRR 0.9518 0.9453 (-0.6%) 0.9349 (-1.7%)
19. System case – Resource Constraint
• Introducing external factors to the system
• Stock availability of recommended items
• The aim is to rank items lower, if less of them are
available
• Minimizing performance loss
20. Simulation
• Online DVD-Rental company
– Operates a warehouse
– Only a limited number of items are available
• Recommend items that are in stock higher in the
ranking list
21. Simulation
• User choice is based purely on recommendation
• Simulating the stock level for 50 days
– Present a list of items to a random number of users
– The probability that the item is taken depends on the
rank
– Cumulative probability depends on how many times the
item was shown and at which rank position
22. Cut-off point
• Threshold c controls the cut-off point from which
the system starts re-ranking items
cp
cps
s ti
titi
ti
,
,,
,
if0
if
24. Evaluation: Monitoring the waiting list size
• Waiting list
– If item is not in stock, user puts it on their waiting list
– When item returns, it goes out to the next user
• Waiting list size represents how long a user has to
wait to get their favourite items
28. Conclusion
• Recommender systems have multiple objectives
• Multiple optimization framework
– Expand the system with minor performance loss
– It is designed to add objectives flexibly
– It can be added to any recommender system
• Two scenarios that offer practical solutions
– Long-tail items
– Stock simulation
29. Future plan
• Personalized digital content delivery
– Reduce delivery cost
• Diversification and the long tail
– Does recommendation kill diversity?
• Evaluate improved user experience
– User studies
31. References
• Deshpande, M., Karypis, G.: Item-based top-N recommendation algorithms.
ACM Trans. Inf. Syst. 22(1) (2004)
• Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic
framework for performing collaborative filtering. In: SIGIR '99. (1999)
• Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for
recommender systems. Computer 42(8) (2009)
• Wang, J., de Vries, A.P., Reinders, M.J.T.: Unifying user-based and item-
based collaborative filtering approaches by similarity fusion. In: SIGIR '06:
Proceedings of the 29th annual international ACM SIGIR conference on
Research and development in information retrieval, New York, NY, ACM Press
Editor's Notes
Background statement
Explain what we mean by multiple objectives in this context
Give two examples
Motivated from the user point of view
Motivated from the system point of view
User point of view would improve the user experience
System point of view would take into account other external factors
Background statement
Explain what we mean by multiple objectives in this context
Give two examples
Motivated from the user point of view
Motivated from the system point of view
User point of view would improve the user experience
System point of view would take into account other external factors
Improving the accuracy of the system does not necessarily equal to improving user experience
Defined the recommender algorithm how good it performs on a metrics and how fast it can do its job
But we need additional factors that would define the whole system
For example if we take a VOD service
User interface affects how user reads, understands find information including the recommendation
How interesting the movies are that the user gets
System related factors include
The underlying hardware of the system
How much, how fast an item can be delivered
External factors include
How much the company earns on an item, for example some items have a higher profit margin
Personalised advertisement, recommending preferred items
User related and system related factors can directly improve user experience
And we suggest that the combination of accuracy and other factors might improve user experience
And the rest can help to maximize profit
Consider accuracy as the main objective
Combine these into a single optimization framework.
Take the prediction values of any baseline algorithm - r-hat.
And add additional constraints that are equally important.
Baseline algorithm to minimize errors.
Higher weights represent higher importance
Without constraints it returns the same as the original order
Recommendation is considered to be a ranking problem
Top-N list
It is a linear optimization problem, so global solution can be found.
Biasing the original order by using constraints
Introducing two case studies to illustrate the use of the framework
The first one is aim to promote items from the long tail.
Assumption
Current systems are biased towards popular items
Picked the first 100 items that have received the highest ratings
Where SVD is likely to place them
Plot the distribution
Figure show the probability that some popular are placed higher
on the recommendation list by all the widely used recommender algorithms
Does that reflect user needs?
We assume that discovering unknown items are more valuable
We aim to identify users who would like alternative choices
And recommend from the long tail for them
Keep recommending popular items if the user has a more mainstream taste
We add this as an inequality constraint to the framework
m is a vector that contains the mainstreamness value of each items in the recommended list
m_u measures the mainstreamness of the user
How these values are calculated are in the paper if you are interested
We added another extra bit to the framework to diversify items
Experimenting with diversification
Promote item from the long tail that differ from each other
Higher covariance
That would reduce the risk of such an extension
Since we approach recommendation as a ranking problem
We used the following IR measures
The probability that popular items ranked higher is significantly reduced
Only 32% of the users have popular items in the top position
The baseline is 45%
We get reduction until position four
Then it is slightly worse than the baseline
This is the case when user studies would provide a better way to evaluate performance
Long tail constraint alone
Long tail constraint with diversification
Slight performance loss for all measures, except one.
The other case – system case
Adding other non-user related factors to the system
the availably of certain items
The aim is to rank items lower if we are about to run out of stock
But also minimize performance loss
The second scenario was evaluated by simulating the stock level
Of an imaginary company for 50 days
We presented a recommendation list to a random number of users each day
The probability that a user took an item depended on the rank
The cumulative probability up to the present point was based on
a, How many times an item was show in the past
B, And at which ranking position
To evaluate the system we monitored the waiting list size
We introduce a cut-off point from which the system will start to reorganize the ranking list.
Adding it as a constraint to the system, in a same fashion
s is a vector that represents the probability that the item is available at the given time
For each item in the list
s_u controls how cautious the system should be with stock level.
e.g if s_u is higher the system starts penalizing items later
As they are getting less available
The experiment is designed to run out of stock
This graph shows the waiting list size for the first 20 days
With respect to parameter c
It controls when the system should start penalizing items as they are running out of stock
e.g if c is set to 0.8 then
after it is 80% likely that an item is taken the system starts penalizing
This table show the average and the maximum performance loss per day
As you can see from c=0.8 and above there is only a slight performance loss of the system.
Two papers
Optimizing algorithm from the user point of view
One way is to identify different errors
A general framework to handle multiple goals
Two scenarios to illustrate that
Improving user experience might be validated using user studies