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Active Learning in Collaborative Filtering Recommender Systems : a Survey

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In collaborative filtering recommender systems user’s preferences are expressed as ratings for items, and each additional rating extends the knowledge of the system and affects the system’s recommendation accuracy. In general, the more ratings are elicited from the users, the more effective the recommendations are. However, the usefulness of each rating may vary significantly, i.e., different ratings may bring a different amount and type of information about the user’s tastes. Hence, specific techniques, which are defined as “active learning strategies”, can be used to selectively choose the items to be presented to the user for rating. In fact, an active learning strategy identifies and adopts criteria for obtaining data that better reflects users’ preferences and enables to generate better recommendations.

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Active Learning in Collaborative Filtering Recommender Systems : a Survey

  1. 1. Active Learning in Collaborative Filtering RSs: a Survey Mehdi Elahi Francesco Ricci Neil Rubens August 2014 Munich, Germany 1 Corresponding journal article: Elahi, Mehdi, Francesco Ricci, and Neil Rubens. "A survey of active learning in collaborative filtering recommender systems." Computer Science Review (2016).
  2. 2. Outline ¤ Introduction ¤ Cold Start Problem ¤ Active Learning in RS ¤ Conclusion and Future Works 2
  3. 3. Introduction ¤ Recommender Systems are tools that support users decision making by suggesting products that are interesting to them. ¤ Collaborative Filtering: A technique used to predict unknown ratings exploiting ratings given by users to items. 3 3 4 2 5 3 ?
  4. 4. Cold Start Problem ¤ New User Problem: when a new user has no rating it is impossible to predict her ratings. 4 3 4 2 5 ? ? ? 3 ? 2 5 ? 3 ? ¤  New item problem: when a new item is added to the catalogue and none has rated this item it will never be recommended.
  5. 5. Active Learning for Collaborative Filtering ¤ Active Learning: ¤ Requests and try to collect more ratings from the users before offering recommendations. 5
  6. 6. Which Items should be chosen? ¤ Not all the ratings are equally useful, i.e., equally bring information to the system. ¤ To minimize the user rating effort only some of them should be requested and acquired. 6
  7. 7. An Illustrative Example 7 Comedy movies Si-Fi movies Obscure movie Zombie movies Which Movie should be proposed to user to rate?
  8. 8. Definition of AL Strategy ¤ An active learning strategy for a Collaborative Filtering is a set of rules to choose the best items for the users to rate. 8
  9. 9. How an AL Strategy works Item Score 1 151 2 44 3 7 4 1 5 42 6 34 7 9 8 55 9 20 … … N 12 System computes the scores for all the items that can be scored (according to a strategy) 9
  10. 10. How an AL Strategy works Top 10 items Score 1 151 8 55 43 54 11 50 2 44 5 42 6 34 22 33 75 29 13 25 The system selects the top 10 items and presents them to the simulated user 10
  11. 11. How an AL Strategy works The items that are rated are added to the train set Rated items 1 2 5 75 13 11
  12. 12. Classifying AL Strategies A.  Personalization: addresses the what extent the personalization is performed when selecting the list of candidate items for the users to rate ¤ Two Classes of Strategies: 12 Non-personalized: are those that ask all the users to rate the same list of items Personalized: ask different users to rate different items – the best for each user.
  13. 13. Classifying AL Strategies 13 Personalization Dimension Corresponding journal article: Elahi, Mehdi, Francesco Ricci, and Neil Rubens. "A survey of active learning in collaborative filtering recommender systems." Computer Science Review (2016).
  14. 14. Classifying AL Strategies B.  Hybridization: whether the strategy takes into account a single heuristic (criterion) for selecting the items or combines several heuristics ¤ Two Classes of Strategies: 14 Single-heuristic: are those that implement a unique item selection rule. Combined-heuristic strategies hybridize single-heuristic strategies by aggregating and combining a number of strategies.
  15. 15. Classifying AL Strategies 15 Hybridization Dimension Corresponding journal article: Elahi, Mehdi, Francesco Ricci, and Neil Rubens. "A survey of active learning in collaborative filtering recommender systems." Computer Science Review (2016).
  16. 16. Non Personalized AL: Classes and Sub-Classes 16
  17. 17. Non Personalized AL: Strategies 17
  18. 18. Example of Non-Personalized AL ¤ Single Heuristics: ¤  Popularity: scores an item according to the frequency of its ratings and then chooses the highest scored items (Carenini, 2003) ¤  Entropy: scores each item with the entropy of its ratings and then chooses the highest scored items (Rashid, 2002 and 2008) ¤ Combined Heuristics: ¤  log(Popularity)*Entropy: combines the popularity and entropy scores and then chooses the highest scored items (Rashid, 2002 and 2008) 18
  19. 19. Personalized AL: Classes and Sub- Classes 19
  20. 20. Personalized AL: Strategies 20
  21. 21. Example of Personalized AL ¤  Single Heuristics: ¤  Decision Tree Based: uses a decision tree whose nodes, represents groups of users. Each node divides the users into three groups based on their ratings: Lovers, Haters, and Unknowns. Starting from the root node, a new user is proposed to rate a sequence of items, until she reaches one of the leaf nodes (Golbandi, 2011) ¤  Binary Prediction: scores an item according to the prediction of its ratings (using transformed matrix of user-item) and then chooses the highest scored items (Elahi, 2011) ¤  Combined Heuristics: ¤  Combined with Voting: scores an item according to the votes given by a committee of different strategies and then chooses the highest scored items (Elahi, 2011) 21
  22. 22. Pros and Cons 22 + simple, fast, no training, serves users with no rating, good for early stage - less accurate, same items for all users + fast, benefits of multiple strategies - flaws of multiple strategies, difficulty of combining properly + accurate, different items for different users, higher prob. of collecting ratings, good for late stage - complex, slow, needs training, cannot serve users with no rating + accurate, great adaptivity to condition of the system - more complex, slowest
  23. 23. Conclusion ¤ We provided a comprehensive review of the state-of- the-art on active learning in collaborative filtering recommender systems ¤ We have classified a wide range of active learning techniques, called Strategies, along the two dimensions: ¤ how personalized these techniques are ¤ how many different item selection criteria (heuristics) are considered by these strategies in their rating elicitation process. 23
  24. 24. Future Works 2424 ¤ To survey works that have been done in AL for other types of recommender systems, such as content-based and context-aware. ¤ To analyze active learning techniques based on their applicability to specific application domains.
  25. 25. Thank you! 25 August 2014 Munich, Germany 25 Corresponding journal article: Elahi, Mehdi, Francesco Ricci, and Neil Rubens. "A survey of active learning in collaborative filtering recommender systems." Computer Science Review (2016).

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