RecSys 2011 Review

Qi Zhao11-01-2011

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

• 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