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Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
Pareto-Efficient Hybridization for Multi-Objective Recommender Systems
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Pareto-Efficient Hybridization for Multi-Objective Recommender Systems

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Presented at RecSys2012, Dublin. Any questions or comments, email me at marcotcr@gmail.com

Presented at RecSys2012, Dublin. Any questions or comments, email me at marcotcr@gmail.com

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  • 1. Pareto-Efficient Hybridization for Multi-Objective Recommender Systems Marco Tulio Ribeiro 1,2 Anisio Lacerda 1,2 Adriano Veloso 1 Nivio Ziviani 1,21 2 Universidade Federal de Minas Gerais Zunnit Technologies Computer Science Department Belo Horizonte, Brazil Belo Horizonte, Brazil ACM Recommender Systems 2012, Dublin, Ireland September 10th, 2012 1
  • 2. Pareto Efficient Hybridization forMulti-Objective Recommender Systems 2
  • 3. Pareto Efficient Hybridization forMulti-Objective Recommender Systems Multi-Objective: 2
  • 4. Pareto Efficient Hybridization forMulti-Objective Recommender Systems Multi-Objective: Accuracy Novelty Diversity 2
  • 5. Pareto Efficient Hybridization forMulti-Objective Recommender Systems Multi-Objective: Accuracy Novelty Diversity Hybridization: Different algorithms have different strengths 2
  • 6. Pareto Efficient Hybridization forMulti-Objective Recommender Systems Multi-Objective: Accuracy Novelty Diversity Hybridization: Different algorithms have different strengths Pareto Efficient: In a moment 2
  • 7. What’s a Good Recommendation? “Good” is a multifaceted concept 3
  • 8. What’s a Good Recommendation? “Good” is a multifaceted concept Are novel recommendations good recommendations? 3
  • 9. Is Novelty Good? 3
  • 10. Is Novelty Good? 3
  • 11. What’s a Good Recommendation? “Good” is a multifaceted concept Are novel recommendations good recommendations? Are accurate recommendations good recommendations? 3
  • 12. Is Accuracy Good? 3
  • 13. Is Accuracy Good? 3
  • 14. What’s a Good Recommendation? “Good” is a multifaceted concept Are novel recommendations good recommendations? Are accurate recommendations good recommendations? Are diverse recommendations good recommendations? 3
  • 15. Is Diversity Good? 3
  • 16. Is Diversity Good? 3
  • 17. Our Work The challenge: Combining multiple algorithms 4
  • 18. Our Work The challenge: Combining multiple algorithms Contributions: Domain and algorithm-independent hybrid 4
  • 19. Our Work The challenge: Combining multiple algorithms Contributions: Domain and algorithm-independent hybrid Multi-objective in terms of accuracy, novelty and diversity. 4
  • 20. Our Work The challenge: Combining multiple algorithms Contributions: Domain and algorithm-independent hybrid Multi-objective in terms of accuracy, novelty and diversity. Adjustable compromise 4
  • 21. Weighted Aggregation Combine the algorithms using standard weighted aggregation 5
  • 22. Weighted Aggregation Combine the algorithms using standard weighted aggregation Problem: finding the vector of weights W 5
  • 23. Weighted Aggregation Combine the algorithms using standard weighted aggregation Problem: finding the vector of weights W Example: W = [SVD: 2.3, TopPop: −5, ItemKNN : 1] 5
  • 24. Weighted Aggregation Combine the algorithms using standard weighted aggregation Problem: finding the vector of weights W Example: W = [SVD: 2.3, TopPop: −5, ItemKNN : 1] Easy to add or remove algorithms 5
  • 25. Evolutionary Algorithms A population is created with a group of random individuals 6
  • 26. Evolutionary Algorithms A population is created with a group of random individuals For each generation: The individuals of the population are evaluated (cross validation) The best individuals are combined, mutated or kept 6
  • 27. Evolutionary Algorithms A population is created with a group of random individuals For each generation: The individuals of the population are evaluated (cross validation) The best individuals are combined, mutated or kept Good for search spaces where little is known 6
  • 28. Evolutionary Algorithms A population is created with a group of random individuals For each generation: The individuals of the population are evaluated (cross validation) The best individuals are combined, mutated or kept Good for search spaces where little is known Domain and algorithm-independent 6
  • 29. SPEA2 Strength Pareto Evolutionary Algorithm [Zitzler, Laumanns and Thiele] 7
  • 30. SPEA2 Strength Pareto Evolutionary Algorithm [Zitzler, Laumanns and Thiele] Multi-Objective Evolutionary Algorithm 7
  • 31. SPEA2 7
  • 32. SPEA2 Strength Pareto Evolutionary Algorithm [Zitzler, Laumanns and Thiele] Multi-Objective Evolutionary Algorithm Uses the Pareto Dominance concept 7
  • 33. Pareto Dominance 7
  • 34. Pareto Dominance 7
  • 35. Pareto Dominance 7
  • 36. Pareto Dominance 7
  • 37. SPEA2 Strength Pareto Evolutionary Algorithm [Zitzler, Laumanns and Thiele] Multi-Objective Evolutionary Algorithm Uses the Pareto Dominance concept Returns a Pareto Frontier 7
  • 38. Pareto Frontier 7
  • 39. SPEA2 Strength Pareto Evolutionary Algorithm [Zitzler, Laumanns and Thiele] Multi-Objective Evolutionary Algorithm Uses the Pareto Dominance concept Returns a Pareto Frontier O(M 2logM ), but performed offline 7
  • 40. Adjusting the System Priority The recommender system may desire to adjust the compromise 8
  • 41. Adjusting the System Priority The recommender system may desire to adjust the compromise We do not return a single solution, but the Pareto Frontier 8
  • 42. Adjusting the System Priority The recommender system may desire to adjust the compromise We do not return a single solution, but the Pareto Frontier Given the priority of each objective, we choose one individual from the frontier 8
  • 43. Adjusting the System Priority 8
  • 44. Evaluation Methodology Task: Top-N Item Recommendation 9
  • 45. Evaluation Methodology Task: Top-N Item Recommendation Evaluation methodology similar to [Cremonesi, Koren and Turrin, RecSys 2010] 9
  • 46. Evaluation Methodology Task: Top-N Item Recommendation Evaluation methodology similar to [Cremonesi, Koren and Turrin, RecSys 2010] With novelty and diversity from [Vargas and Castells, RecSys 2011] 9
  • 47. Datasets Movielens Last.fmRecommends movies musicUsers 6,040 992Content 3,883 movies 176,948 artistsRatings/Feedback 1,000,209 19,150,868Feedback explicit implicit Table: Summary of Datasets 10
  • 48. Recommendation Algorithms PureSVD (50 and 150 factors) [Cremonesi, Koren and Turrin, RecSys 2010] 11
  • 49. Recommendation Algorithms PureSVD (50 and 150 factors) [Cremonesi, Koren and Turrin, RecSys 2010] KNNs: Item and User-based 11
  • 50. Recommendation Algorithms PureSVD (50 and 150 factors) [Cremonesi, Koren and Turrin, RecSys 2010] KNNs: Item and User-based Most Popular 11
  • 51. Recommendation Algorithms PureSVD (50 and 150 factors) [Cremonesi, Koren and Turrin, RecSys 2010] KNNs: Item and User-based Most Popular WRMF [Hu et al, ICDM 2008, Pan et al ICDM 2008] 11
  • 52. Recommendation Algorithms PureSVD (50 and 150 factors) [Cremonesi, Koren and Turrin, RecSys 2010] KNNs: Item and User-based Most Popular WRMF [Hu et al, ICDM 2008, Pan et al ICDM 2008] Content-based: Item Attribute KNN (movielens only) User Attribute KNN 11
  • 53. Hybrid Baselines Borda Count 12
  • 54. Hybrid Baselines Borda Count STREAM (stacking-based approach) [Bao, Bergman and Thompson, RecSys 2009] 12
  • 55. Hybrid Baselines Borda Count STREAM (stacking-based approach) [Bao, Bergman and Thompson, RecSys 2009] Weighted aggregation with equal weights 12
  • 56. Some of Our Solutions - Movielens PO-acc: PO-acc2: PO-nov: PO-div: 13
  • 57. 14
  • 58. 15
  • 59. 16
  • 60. Some of Our Solutions - Last.fm PO-acc: PO-nov: PO-div: 17
  • 61. 18
  • 62. 19
  • 63. 20
  • 64. Conclusions A multi-objective hybridization technique for combining recommendation algorithms 21
  • 65. Conclusions A multi-objective hybridization technique for combining recommendation algorithms “Tune” the system to different priority needs 21
  • 66. Conclusions A multi-objective hybridization technique for combining recommendation algorithms “Tune” the system to different priority needs Highly reproducible experiments: Public datasets Open-source implementations (MyMediaLite, DEAP) 21
  • 67. Conclusions A multi-objective hybridization technique for combining recommendation algorithms “Tune” the system to different priority needs Highly reproducible experiments: Public datasets Open-source implementations (MyMediaLite, DEAP) Competitive with the best algorithms according to each objective 21
  • 68. Future Work Test these assumptions using online AB-testing, in real world E-commerce websites 22
  • 69. Future Work Test these assumptions using online AB-testing, in real world E-commerce websites Try maximizing other objectives: profit, stock diversity, etc 22
  • 70. Future Work Test these assumptions using online AB-testing, in real world E-commerce websites Try maximizing other objectives: profit, stock diversity, etc Figuring out how often the weights need to be re-adjusted 22
  • 71. Pareto-Efficient Hybridization for Multi-Objective Recommender Systems Marco Tulio Ribeiro 1,2 Anisio Lacerda 1,2 Adriano Veloso 1 Nivio Ziviani 1,21 2 Universidade Federal de Minas Gerais Zunnit Technologies Computer Science Department Belo Horizonte, Brazil Belo Horizonte, Brazil ACM Recommender Systems 2012, Dublin, Ireland September 10th, 2012 23

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