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WWW 2009 Presented by Wang Dingyan Tagommenders: Connecting Users to Items through Tags
Problem Related Work Algorithms & Results Inferring Tag Preference Tag-based Recommenders Contributions Outline
Problem • Connecting Users to Items through Tags
Related Work 1 • User-based • Given a particular user, recommend movies that similar users like. • Item-based • Predict users’ ratings for an item based on their ratings for similar items.
Related Work 2 • Two trends during the Netflix Prize(an ongoing open competition for the best collaborative filtering algorithm to predict user ratings for films, based on previous ratings) • SVD: singular value decomposition algorithm • Combination of the output of multiple recommender algorithms to improve performance
Related Work 3 • Collaborative filtering algorithms • User-based, item-based, SVD • Shortcomings: do not use data about items. “Toy Story” is an animated movie. They don’t know! • Content-based • Combination of the two above
Related Work 4 • Stand on the shoulders of giants • Two ways of extension in this paper • Explore how estimates of tag quality improve recommender performance • Automatically learn relationships between tags and movies based on inferred tag preferences and movie ratings
Quantitative Contributions • Investigate 11 different signals of a user’s interest in tags(tag searches, item ratings…) • Explore 5 different algorithms for calculating item preferences based on tag preferences • Conduct an empirical evaluation using 118,017 star-ratings of tag preference and 1,720,390 star-ratings of item preference.
Inferring tag preference • Can systems infer users’ preferences for tags? • Users’ direct interactions with the tag. • Users’ interactions with items having the tag.
Inferring preference using Tag Signals • Tag-applied • Tag-searched • Tag-quality • Learning to Recognize Quality Tags, In Proceedings of IUI, 2009. • Least-squares regression
Inferring Preference using Item Signals • Movie-clicks • Movie-log-odds-clicks • Movie-r-clicks • Movie-r-log-odds-clicks • Movie-ratings • Movie-bayes
Inferring Preference using Item Signals • Movie-clicks • Movie-log-odds-clicks • Movie-r-clicks • Movie-r-log-odds-clicks ClickRate
Inferring Preference using Item Signals • Movie-ratings • Movie-bayes
Explicit Algorithms • Use users' movie ratings • Recommend and predict • 3 algorithms • Cosine-tag • Linear-tag • Regress-tag Tag-based Recommenders Implicit Algorithms Tag data only Recommend only 2 algorithms Implicit-tag Implicit-tag-pop
Implicit : Implicit-tag Vector Space Model Inferred tag preference Relevance weight
Implicit : Implicit-tag-pop Implicit-tag with movie popularity Tag > clicks, clicker count > click count Linear estimation of log function
Explicit Algorithms • Use users' movie ratings • Recommend and predict • 3 algorithms • Cosine-tag • Linear-tag • Regress-tag Recommenders Implicit Algorithms Tag data only Recommend only 2 algorithms Implicit-tag Implicit-tag-pop
Explicit : Cosine-tag Cosine similarity: rating vs tag preference
Explicit : Linear-tag Least-square fit linear regression
Explicit : Regress-tag Linear-tag with similarity between tags SVM was best to estimate h Robustness against overfitting
Contributions • Algorithms that infer users’ preferences for tags. • Tag-based recommendation algorithms that infer users’ preferences for movies based on their inferred preferences for tags. • Evaluate the end-to-end predictive performance of tagommender algorithms that combine tag preference inference algorithms with tag-based recommenders.
What for? • Help sites with an abundance of tagging activity to improve item recommendation. • Offer a flexible and comprehensible alternative to traditional recommender systems.
Q&A Thank you!
Tag-Based Recommenders • Implicit Tag-Based Algorithm • Implicit-tag • Implicit-tag-pop
Tag-Based Recommenders • Explicit Tag-Based Algorithm • Cosine-tag
Tag-Based Recommenders • Explicit Tag-Based Algorithm • Linear-tag
Tag-Based Recommenders • Explicit Tag-Based Algorithm • Regress-tag