1. 6/10/14 27th Canadian Conference on Artificial Intelligence Sistemas de Recomendação Hibridos baseados em Mineração de Preferências “pairwise” Data Mining AULA 19 – Parte II Sandra de Amo

2. 6/10/14 27th Canadian Conference on Artificial Intelligence Referência AI2014 Sandra de Amo, Cleiane Gonçalves: Towards a Tunable Framework for Recommendation Systems based on Pairwise Preference Mining Algorithms, 27th Canadian Conference on Artificial Intelligence, May 2014, Montreal, Canada.

3. Towards a Tunable Framework for Recommendation Systems based on Pairwise Preference Mining Algorithms Cleiane Gonçalves Oliveira and Sandra de Amo Federal University of Uberlandia – Brazil deamo@ufu.br Federal University of Uberlandia Laboratory of Information Systems

4. General Purpose Popular Recommendation Systems: Main goal: to predict user ratings for yet unseen items. Classifications techniques: ratings viewed as classes In some domains: users evaluate an item by comparing it with other items already evaluated. Drawback 1: Classifiers classify items isolatedly. Poor accuracy. Drawback 2: no high ratings are predicted no recommendations 6/10/ 14 27th Canadian Conference on Artificial Intelligence 4

5. 6/10/14 27th Canadian Conference on Artificial Intelligence General Purpose We argue: More interesting: to predict a ranking of top-k items, whatever the ratings they may be given by the user. Input data: a “preference graph” (pairs of items (a,b): a is preferred to b) Preference Mining Task : given two new items c, d which one is preferred by the user ? Preference graph  ranking on the nodes

6. Main Contribution 6/10 /14 27th Canadian Conference on Artificial Intelligence 6 A general framework for implementing Recommendation Systems based on Preference Mining and Preference Aggregation techniques Phase 1 : Building the Recommendation Model (offline)

7. 6/10/ 14 27th Canadian Conference on Artificial Intelligence 7 Phase 2 : Making Recommendations (online)

8. Module 1: Preference Representation 6/10/ 14 27th Canadian Conference on Artificial Intelligence 8 Ratings provided by user u Preference matrix for user u Each user u is represented by its preference matrix Mu The element i,j in the matrix is the degree of preference of user u on item i over item j. Calculated as where h must satisfy certain conditions (Chiclana et al. 2001) The family verifies such conditions

9. 6/10 /14 27th Canadian Conference on Artificial Intelligence 9 Phase 1 : Building the Recommendation Model (offline)

10. Module 2: Profiles Construction 6/10/ 14 27th Canadian Conference on Artificial Intelligence 10 Mu1 Mu2 Mu3 Cluster 1 Mu4 Mu5Mu6 Cluster 2 Mu7Mu8Mu9 Cluster 3 Users inside a given cluster have similar taste.

11. 6/10 /14 27th Canadian Conference on Artificial Intelligence 11 Phase 1 : Building the Recommendation Model (offline)

12. Module 3: Preference Aggregation 6/10/ 14 27th Canadian Conference on Artificial Intelligence 12 Mu1Mu2Mu3 A consensus preference matrix θ Cluster of similar matrices Aggregation Operator A unique consensus matrix θ is associated to each cluster

13. 6/10 /14 27th Canadian Conference on Artificial Intelligence 13 Phase 1 : Building the Recommendation Model (offline)

14. Module 4 : Preference Mining 6/10/ 14 27th Canadian Conference on Artificial Intelligence 14 A consensus preference matrix M Mining Algorithm Preference Model A Preference Model is any function capable to predict, given two items i1 and i2 which one would be preferred by a user whose preference matrix is M Two Preference Mining algorithms have been tested : CPrefMiner (de Amo et al. ICTAI 2012) and CPrefMiner* (de Amo et al., 2014 to appear) The Preference Model produced by CPrefMiner and CPrefMiner* = set of preference rules of the form: IF THEN I prefer `this’ to `that’ Ex. : IF Director = `Spielberg’ THEN I prefer Genre = Action to Genre = Drama

15. 6/10 /14 27th Canadian Conference on Artificial Intelligence 15 Phase 1 : Building the Recommendation Model (offline)

16. Module 5: The Recommendation Process (online) 6/10/ 14 27th Canadian Conference on Artificial Intelligence 16 Consensus θ1 + Preference Model 1 Consensus θ2 + Preference Model 2 Consensus θ3 + Preference Model 3 The Recommendation Model M How M recommends Items to a new user u ? 1. u must evaluate some few items i1, i2, …, in 2. The preference matrix Mu for u (very sparse) is built 3. Mu is compared to the consensus matrices θ1,…, θn 4. The closest consensus is found : θ* 5. The Preference Model associated to θ* is used to produce a ranking of top-k most preferred items.

17. Experiments Set-up 296 users ; 262 movies User-movie ratings from the Group Lens Project : (userId, filmId, rating) Details on films from IDMB website (filmId, Genre, Actors, Director, Year, Language) Total of evaluations: 67,971 Complete : 296 * 262 = 77,553 5-cross validation on users and on items Datasets Experiment Protocol 6/10/14 27th Canadian Conference on Artificial Intelligence 17

18. The XPrefRec instantiation 6/10/14 27th Canadian Conference on Artificial Intelligence 18 cosine

19. Some results Performance Execution Time 6/10/ 14 27th Canadian Conference on Artificial Intelligence 19 Baseline: CBCF (Melville et al., AAAI 2002) Hybrid recommendation system: content-based + collaborative filtering Uses classification techniques for predicting user ratings

20. Conclusion and Future Work In this paper we proposed PrefRec : A general framework for implementing Recommendation Systems Hybrid Approach: content-based + collaborative filtering Preference Mining and Preference Aggregation techniques. Four modules Flexible – can incorporate new algorithms in each module Future Work A more rigorous factor design of PrefRec To study the effects of the different factors involved at each module. 6/10/14 27th Canadian Conference on Artificial Intelligence 20