Recommender Systems
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This document provides an overview of recommender systems. It defines recommender systems as tools that suggest items like books, movies, music or products to users based on their preferences and behavior. It then describes several common types of recommender system algorithms including content-based, collaborative filtering using similarity measures, and latent factor models. The document also briefly discusses open problems in recommender systems like how to evaluate and improve different evaluation metrics over time.
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Recommender Systems
- 2. Who am I? Chu-Yu Hsu Data Scientist @ IBM Taiwan Dedicated to Recommender System rio512hsu@gmail.com https://github.com/ChuyuHsu
- 3. Outline • What is Recommender System • Related Algorithms • Content Based Algorithms • Collaborative Filtering (CF) • Latent Factor Model • Going Any Further
- 4. Item Database User Query Search Suggestion
- 6. • More choices necessitate better filters • Example: • Books, movies, music, news articles, products • People
- 7. Types of Recommenders • Editorial and hand curated • Simple aggregates • Tailored to individual users
- 9. Netflix Prize • An open competition to predict user ratings for films • Algorithms are evaluated in the Root Mean Squared Error (RMSE)
- 10. Approaches • Content based • Collaborative • Laten factor model
- 11. Content Based Recommender Main idea: Recommend items to customer x similar to previous items rated highly by x
- 12. Pros 1.No need for data of other users 2. Able to recommend to users with unique tastes 3. Able to recommend new & unpopular items 4. Explanations for recommendations Cons 1.Finding appropriate features is hard 2. Overspecialisation 3. Cold-start for new users
- 13. Collaborative Filtering Main idea: Find set N of other users whose ratings are “similar” to X’s ratings
- 14. Similarity • Jaccard Similarity • Cosine Similarity • Centered Cosine Similarity Normalize ratings by subtracting row mean Also known as Pearson Correlation
- 15. Rating Predicting • User Based CF • Item Based CF
- 16. Item Based v.s. User Based • In theory user based CF and item based CF are dual • Item based CF usually outperforms user-based in many use cases • Items are "simpler" than users • Items belong to a small set of "genres", users have varied tastes • Item similarity is more meaningful than User Similarity
- 17. Latent Factor Model • For now let’s assume we can approximate the rating matrix
- 18. • SVD should be a intuitive choice • But R has missing entries • SVD assumes all missing entries are zero • Ignore the missing entries • Forget to be orthogonal/unit length
- 19. • Our goal is to find P and Q such that (Sum of Square Error): • Root Mean Square Error (RMSE)
- 20. Alternative Least Squares • Because p and q are both unknown, the object function is not convex • If fix one of the unknowns -> can be solved as a least squares problem
- 21. Overfitting • To solve overfitting we introduce regularization: • Allow rich model where there are sufficient data • Shrink aggressively where data are scarce
- 22. What’s More • Prediction accuracy won’t always be the most important • Recentness • Novelty • Explanation based diversity • Temporary diversity
- 23. What’s More • All kind of user behaviors
- 24. Open Problems • How to weight different behaviors • How to improve deferent metrics • How to evaluate and evolve
- 25. References • Anand Rajaraman and Jeffrey David Ullman. 2011. Mining of Massive Datasets. Cambridge University Press, New York, NY, USA. • 项亮. 2012. 推荐系统实践. ⼈人⺠民邮电出版社, 北京
- 26. – Jeffrey M. O’Brien, CNN Money “The Age of Search has come to an end. Long live the recommendation!”