Sistemas de Recomendação Hibridos baseados em Mineração de Preferências “pairwise”

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

2. 27th Canadian Conference on Artificial Intelligence • 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. Referência

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

4. General Purpose 6/10/14 • 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 27th Canadian Conference on Artificial Intelligence 4

5. 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 A general framework for implementing Recommendation Systems based onPreference Mining andPreference Aggregation techniques Phase 1 : Building the Recommendation Model (offline) 27th Canadian Conference on Artificial Intelligence 6

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

8. Module 1: Preference Representation 6/10/14 Preference matrix for user u Ratings provided by user u 27th Canadian Conference on Artificial Intelligence • 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 8

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

10. Mu4 Mu5 Mu1 Mu2 Mu6 Mu3 Mu7 Mu8 Mu9 Module 2: Profiles Construction 6/10/14 27th Canadian Conference on Artificial Intelligence Cluster 2 Cluster 1 10 Users inside a given cluster have similar taste. Cluster 3

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

12. Mu1 Mu2 Mu3 Module 3: Preference Aggregation 6/10/14 27th Canadian Conference on Artificial Intelligence Aggregation Operator A consensus preference matrix θ Cluster of similar matrices 12 A unique consensus matrix θ is associated to each cluster

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

14. Module 4 : Preference Mining 6/10/14 Mining Algorithm Preference Model A consensus preference matrix M • A Preference Model is anyfunctioncapable 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 <context> THEN I prefer `this’ to `that’ Ex. : IF Director = `Spielberg’ THEN I prefer Genre = Action to Genre = Drama 27th Canadian Conference on Artificial Intelligence 14

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

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

17. Experiments Set-up 6/10/14 Datasets • 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 27th Canadian Conference on Artificial Intelligence Experiment Protocol 17

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

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

20. Conclusion and Future Work 6/10/14 • 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. 27th Canadian Conference on Artificial Intelligence 20