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RecSys 2011 Review

RecSys 2011 Review. Qi Zhao 11-01-2011. Outline. Overview Sessions Algorithms Recommenders and the Social Web Multi-dimensional Recommendation, Context-awareness and Group Recommendation Methodological Issues, Evaluation Metrics and Tools Human factors Emerging Recommendation Domains

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RecSys 2011 Review

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  1. RecSys 2011 Review Qi Zhao 11-01-2011

  2. Outline • Overview • Sessions • Algorithms • Recommenders and the Social Web • Multi-dimensional Recommendation, Context-awareness and Group Recommendation • Methodological Issues, Evaluation Metrics and Tools • Human factors • Emerging Recommendation Domains • Conclusion

  3. Overview • Participants • Student, professor • Research Institutes, like Yahoo! Research, eBay Research, Microsoft Research, etc • Industry. Twitter, Google, Facebook, Netflix, LinkedIn, etc • Oral papers, posters, workshops, demos • Themes • Algorithm • Recommendation and the Social Web • Multi-Dimensional Rec, Group Rec, Context-Aware Rec • Evaluation Metric • Human factors • Emerging Domains

  4. Session: Algorithm • Major issues to tackle • Cold start

  5. Generalizing Matrix Factorization Through Flexible Regression Priors • Motivation • Warm-start scenario: low-rank factorization + regularization • Zero-mean regularization • Handle cold-start scenario • New users • Approach • GMF • Regularization based on Non-linear regression on user /item feature

  6. Shared Collaborative Filtering • How it works? • Leverage the data from other parties to improve own CF performance • Issues • Privacy concerns when sharing the community data

  7. Session: Recommender Systems and the Social Web

  8. Recommendation in Social Rating Networks • Social Rating Network • User-user relationship • User express ratings over some items • Example: Epinions, Flixter, • Why use social networks in recommendation? • Selection and social influences by sociologist • Selection: tendency to relate to people with similar attributes • SNR: similar rating behavior • Social influence: adopting ratings from friends • Selection and social influence drive the formation of like-minded and well-connected users. • Challenges • Mixed groups, social relations • Generalized Stochastic Block Model • Mixed group membership for both users and items

  9. Personalized PageRank Vectors for Tag Recommendations: Inside FolkRank • Setting: Folksonomy • User, Tags, Resources(flickr, del.icio.us, etc) • User assign tags to resources. • Problem • Ranking tag, user and resource • Tag recommendation • Main contribution • Present and formalize the FolkRank model • Present FolkRank-like model which provides fast tag recommendation

  10. Session: Multi-dimensional Recommendation, Context-awareness and Group Recommendation

  11. Multi-Criteria Service Recommendation Based on User Criteria Preference • Using multiple criteria to value the product or service • E.g. Restaurant – price, location, quality of food, service speed, etc • User has her own preference over the attributes • Cluster users based on their preference • Prediction based on users within the same cluster

  12. The Effect of Context-Aware Recommendations on Customer Purchasing Behavior and Trust • Content-Aware Recommendation Systems(CARS) • Additional information like location, time, your companies, etc • Effect on Purchasing Behavior • Accuracy • Trust. Recommendation should be credible and objective. • Methodology • Controlled experiment • Three methods: content-based, CARS, random • Metric: accuracy, diversity(entropy) • Purchasing change: Money spend on the product

  13. Group Recommendation • Recommendations for a group of people instead of individuals • E.g. people sitting around watching tv • The challenge • Aggregated preference might be diverse • Depend on the group’s characterizer • Homogeneous or Heterogeneous • Similar demographic information or not

  14. Session: Methodological Issues, Evaluation Metrics and Tools

  15. OrdRec: An Ordinal Model for Predicting Personalized Item Rating Distribution • Common views upon feedbacks • Numerical values • Apply Collaborative Filtering • About numerical ratings • Different users have their own internal scale • Hard to assign a numerical value • Ranking products through comparing • Humans are more consistent when comparing products than giving absolute scores • Ordinal • Express relative preference over items • Evaluation • RMSE • Fraction of Concordant Pairs(FCP) • OrdRec outperforms existing approaches: SVD++, RBM, MultiMF

  16. Session: Human factors

  17. A User-Centric Evaluation Framework for Recommender Systems • ResQue(Recommender system’s Quality of User Experience) • Understanding issues of RecSys • Evaluation Layers • Perceived system qualities • User’s belief • Subjective attitude • Behavioral intention • Experiment Design • Survey on 239 participants

  18. Cont.

  19. Session: Emerging Domains • Yahoo! Music Recommendation: Modeling Music Ratings with Temporal Dynamics and Item Taxonomy • CrimeWalker: A recommendation Model for Suspect Investigation • Personalized Activity Stream: Sifting through the “River of News”

  20. Conclusion • Modeling the Recommendation • Collaborative Filtering • Incorporating additional features • Evaluation Metrics • Accuracy, Diversity, Novelty, etc • Adapt to Constantly Changing Internet Ecosystem • Social Network • Realtime Activity Stream

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