RecSys 2011 Review Qi Zhao

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

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

Session: Algorithm Major issues to tackle – Cold start

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

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

Session: Recommender Systems and the Social Web

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

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

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

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

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

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

Session: Methodological Issues, Evaluation Metrics and Tools

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

Session: Human factors

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

Cont.

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”

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