Introduction and new trends in Recommender Systems
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Presentation at the Club of Developers, Bari, Italy
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Introduction and new trends in Recommender Systems
- 1. Introduction and new trends in Recommender Systems Paolo Tomeo @paotomeo
- 5. Recommender Systems “Software tools and techniques that provide suggestions for items that are most likely of interest to a particular user”
- 9. User – Item Interaction Rating Like Click Visualization Search query
- 10. Ratings
- 12. Suggerisce all'utente item simili a quelli che ha apprezzato in passato Approaches Content Based filtering Collaborative filtering Hybrid approaches
- 13. Suggerisce item apprezzati da altri utenti che hanno preferenze simili Content based filtering Recommendations based on items similar to the ones that the user liked in the past Strengths user independence explainability useful for cold-start Drawbacks sensitive to bad or incomplete information over-specialization less novelty and discovery
- 14. Suggerisce item apprezzati da altri utenti che hanno preferenze simili Collaborative filtering Recommendations based on items that other users with similar tastes liked in the past Strengths independent from the content typically more accurate can promote discovery Drawbacks sensitive to the quantity of users and feedbacks difficult to recommend new item (cold-start item) can reinforce item popularity
- 15. Suggerisce item apprezzati da altri utenti che hanno preferenze simili Matrix factorization CF
- 16. Hybrid approaches Combination of content-based and collaborative filtering methods Ensemble of different methods Graph-based methods applied on heterogeneous networks Feature combination -> (Matrix Factorization with side information, Factorization Machines, Neural Networks, …)
- 18. Offline evaluation 1 - Choose a dataset 2 - Split feedbacks for each user in train, validation and test sets 3 - Train the systems with the evaluation set 4 - Produce the recommendations 5 - Evaluate on the test set
- 19. Some Libraries RankSys - Java 8 Recommender Systems framework for novelty, diversity and much more https://github.com/RankSys/RankSys Rival - Java toolkit for recommender system evaluation https://github.com/recommenders/rival GraphLab Create - Python machine learning framework https://turi.com/products/create
- 20. (Some) New trends Deep learning Wide and deep learning Multi-criteria Graph-based algorithms Use of Semantic Web
- 21. Deep learning P. Covington, J. Adams, E. Sargin. “Deep Neural Networks for YouTube Recommendations”
- 22. Wide and deep learning https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html https://www.tensorflow.org/versions/r0.11/tutorials/wide_and_deep/index.html
- 23. Multi-Criteria
- 25. Use of Semantic Web
- 26. Thanks!
Editor's Notes
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