RecSys 2011 Review
Qi Zhao
11-01-2011
Outline
• Overview
• Sessions
– Algorithms
– Recommenders and the Social Web
– Multi-dimensional Recommendation, Contextawareness 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 wellconnected 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