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Enhancing Item Recommendations with Tagommenders Algorithm

Explore how the Tagommenders algorithm connects users to items through tags, inferring user preferences and improving recommender system performance. The algorithm combines tag preference inference with tag-based recommenders, offering a flexible alternative to traditional systems. It investigates various signals of user interest in tags and item preferences, with quantitative contributions and empirical evaluations. Discover the explicit and implicit algorithms, such as Cosine-tag, Linear-tag, and Regress-tag, robust against overfitting. Evaluate the end-to-end predictive performance and the robustness of the SVM algorithm. Enhance recommendation systems by inferring users' tag and movie preferences, leading to improved item recommendations on platforms with tagging activities.

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Enhancing Item Recommendations with Tagommenders Algorithm

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  1. WWW 2009 Presented by Wang Dingyan Tagommenders: Connecting Users to Items through Tags

  2. Problem Related Work Algorithms & Results Inferring Tag Preference Tag-based Recommenders Contributions Outline

  3. Problem • Connecting Users to Items through Tags

  4. 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.

  5. 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

  6. 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

  7. 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

  8. 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.

  9. Dataset

  10. Tagommenders

  11. Inferring tag preference • Can systems infer users’ preferences for tags? • Users’ direct interactions with the tag. • Users’ interactions with items having the tag.

  12. Inferring preference using Tag Signals • Tag-applied • Tag-searched • Tag-quality • Learning to Recognize Quality Tags, In Proceedings of IUI, 2009. • Least-squares regression

  13. Inferring Preference using Item Signals • Movie-clicks • Movie-log-odds-clicks • Movie-r-clicks • Movie-r-log-odds-clicks • Movie-ratings • Movie-bayes

  14. Inferring Preference using Item Signals • Movie-clicks • Movie-log-odds-clicks • Movie-r-clicks • Movie-r-log-odds-clicks ClickRate

  15. Inferring Preference using Item Signals • Movie-ratings • Movie-bayes

  16. Results

  17. 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

  18. Implicit : Implicit-tag Vector Space Model Inferred tag preference Relevance weight

  19. Implicit : Implicit-tag-pop Implicit-tag with movie popularity Tag > clicks, clicker count > click count Linear estimation of log function

  20. 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

  21. Explicit : Cosine-tag Cosine similarity: rating vs tag preference

  22. Explicit : Linear-tag Least-square fit linear regression

  23. Explicit : Regress-tag Linear-tag with similarity between tags  SVM was best to estimate h Robustness against overfitting

  24. Results - Top5

  25. Results - MAE

  26. 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.

  27. What for? • Help sites with an abundance of tagging activity to improve item recommendation. • Offer a flexible and comprehensible alternative to traditional recommender systems.

  28. Q&A Thank you!

  29. Tag-Based Recommenders • Implicit Tag-Based Algorithm • Implicit-tag • Implicit-tag-pop

  30. Tag-Based Recommenders • Explicit Tag-Based Algorithm • Cosine-tag

  31. Tag-Based Recommenders • Explicit Tag-Based Algorithm • Linear-tag

  32. Tag-Based Recommenders • Explicit Tag-Based Algorithm • Regress-tag

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