XPLODIV: An Exploitation-Exploration Aware Diversification Approach for Recommender Systems

1. XPLODIV: An Exploitation-Exploration Aware Diversification Approach for Recommender Systems Andrea Barraza-Urbina, Benjamin Heitmann, Conor Hayes, Angela Carrillo-Ramos The 28th International FLAIRS Conference May 18-20, 2015 Hollywood, Florida, USA Pontificia Universidad Javeriana Facultad de Ingeniería Maestría en Ingeniería de Sistemas y Computación Bogotá, Colombia The Insight Centre for Data Analytics Unit for Information Mining and Retrieval (UIMR) National University of Ireland Galway, Ireland

2. Centre for Data Analytics Agenda Introduction XPLODIV Diversification Approach Conclusion and Future Work Experimental Validation Literature Review 2

3. Centre for Data Analytics Introduction Conclusion and Future Work Experimental Validation Literature Review 3 XPLODIV Diversification Approach Agenda

4. 4 >50% of Job Applications are due to Recommendation ~75% of Watched Movies are due to Recommendation Tools that help users identify interesting products by means of personalized suggestions. Discovery Recommender Systems

5. 5 User-Item Matrix Rating Recommender Systems

6. Centre for Data Analytics The task of selecting a subset of k elements from a broader set S in order to maximize an objective function that considers both the relevance and diversity of the k elements. DiversityRelevance A set is diverse if there is a high level of heterogeneity (dissimilarity) between the items in the collection. 6 The Diversification Problem

7. Centre for Data Analytics Search Space User Profile Recommend 10 movies to a user… Movie Recommendation System 7 The Diversification Problem Comedy ActionDrama

8. Centre for Data Analytics What happens if the user is no longer interested in Action movies? Organize by relevance… 8 The Diversification Problem User Profile Comedy ActionDrama

9. Centre for Data Analytics VS. Diversity -Variety -Balance -Disparity Relevance Relevance Diversity In response to user profile ambiguity and the redundancy among results… 9 The Diversification Problem

10. Centre for Data Analytics • Offering items representative of the variety of the user’s tastes. • Offering novel products to explore unknown user preferences. • Novelty can be achieved depending on how far or diverse an item is from the user’s past experience. Discovery Exploitation of the User Profile Exploration of novel products 10 Exploitation vs. Exploration

11. Centre for Data Analytics Design a diversification technique that: Research Goal

12. Centre for Data Analytics XPLODIV Diversification Approach Conclusion and Future Work Experimental Validation Literature Review 12 Introduction Agenda

13. Centre for Data AnalyticsAnalysis of Diversification Techniques Information Retrieval Recommender Systems [Carb98] [Agra09] [Sant10] [Zhen12] [Smyt01] [Zieg05] [Varg12] [Adom09] Type of Solution Greedy Optimization - Explicit Approach - Implicit Approach - Trade-off diversity vs. relevance Control of diversity vs. relevance trade-off ? ? Trade-off exploitation vs. exploration Encourages Discovery ? ? ? ? Control of exploitation vs. exploration trade-off - 13

14. Centre for Data Analytics Information Retrieval Recommender Systems [Carb98] [Agra09] [Sant10] [Zhen12] [Smyt01] [Zieg05] [Varg12] [Adom09] Type of Solution Greedy Optimization - Explicit Approach - Implicit Approach - Trade-off diversity vs. relevance Control of diversity vs. relevance trade-off ? ? Trade-off exploitation vs. exploration Encourages Discovery ? - - - ? ? - ? Control of exploitation vs. exploration trade-off - - - - - Analysis of Diversification Techniques 14 Control of diversity vs. relevance trade-off

15. Centre for Data AnalyticsAnalysis of Diversification Techniques Information Retrieval Recommender Systems [Carb98] [Agra09] [Sant10] [Zhen12] [Smyt01] [Zieg05] [Varg12] [Adom09] Type of Solution Greedy Optimization + + + + + + + - Explicit Approach - + + + - - + - Implicit Approach + - - - + + - - Trade-off diversity vs. relevance Control of diversity vs. relevance trade-off + - + + + + ? ? Trade-off exploitation vs. exploration Encourages Discovery ? ? ? - ? Control of exploitation vs. exploration trade-off - 15 Control of Exploitation vs. Exploration trade-off Encourages Discovery Current solutions are mostly inspired by work in Information Retrieval

16. Centre for Data Analytics XPLODIV Diversification Approach Conclusion and Future Work Experimental Validation Literature Review 16 Introduction Agenda

17. Centre for Data Analytics Traditional Recommendation Algorithm Candidate Items Final Diversified Recommendation List User Profiles Item Profiles XPLODIV: Exploitation-Exploration Diversification Technique Diversification Technique XPLODIV We formulate our approach as a: • Post-Filtering Technique • Greedy optimization problem 17

18. Centre for Data Analytics XPLODIV 𝑖, 𝕌, ℝ = 𝛼 ∙ 𝑟𝑒𝑙 𝑖 + 1 − 𝛼 ∙ 𝑑𝑖𝑣 𝑖, ℝ ∙ 𝛽 ∙ 𝑥𝑝𝑙𝑜𝑖𝑡(𝑖, 𝕌) + 1 − 𝛽 ∙ 𝑥𝑝𝑙𝑜𝑟𝑒 𝑖, 𝕌 XPLODIV has four core dimensions: Relevance 𝑟𝑒𝑙 𝑖 Diversity 𝑑𝑖𝑣 𝑖, ℝ Exploitation 𝑥𝑝𝑙𝑜𝑖𝑡 𝑖, 𝕌 Exploration 𝑥𝑝𝑙𝑜𝑟𝑒 𝑖, 𝕌 • Each dimension must be normalized to return a value in the range [0,1]. • 1 is the highest desirable value. 18 XPLODIV

19. Centre for Data Analytics XPLODIV 𝑖, 𝕌, ℝ = 𝛼 ∙ 𝑟𝑒𝑙 𝑖 + 1 − 𝛼 ∙ 𝑑𝑖𝑣 𝑖, ℝ ∙ 𝛽 ∙ 𝑥𝑝𝑙𝑜𝑖𝑡(𝑖, 𝕌) + 1 − 𝛽 ∙ 𝑥𝑝𝑙𝑜𝑟𝑒 𝑖, 𝕌 The approach has two control parameters: • The parameter 𝜶 controls the trade-off between relevance and diversity. • The parameter 𝜷 controls the trade-off between exploitation and exploration. 19 XPLODIV

20. Centre for Data Analytics The relevance dimension gives priority to items that have high predicted rating. How relevant is the item we are evaluating? 20 XPLODIV: Relevance Dimension Normalized Predicted Rating

21. Centre for Data Analytics Average pairwise dissimilarity of an element i to a set ℝ Minimum distance of an element i to a set ℝ How distant is the item being evaluated from those previously selected? 21 XPLODIV: Diversity Dimension The diversity dimension measures how diverse an item i is in relation to a set of items ℝ.

22. Centre for Data Analytics The exploitation dimension gives priority to items that exploit known user preference information. Probability of high rating of similar items How representative is the item being evaluated of items found in the user profile? 22 XPLODIV: Exploitation Dimension

23. Centre for Data Analytics Diversity of item i to user profile 𝕌 Average pairwise dissimilarity Minimum dissimilarity How novel is the item being evaluated for the user? 23 XPLODIV: Exploration Dimension The exploration dimension gives priority to items that allow the user to discover and explore the unknown.

24. Centre for Data Analytics 24 XPLODIV 𝜶 XPLODIV 𝑖, 𝕌, ℝ = 𝛼 ∙ 𝑟𝑒𝑙 𝑖 + 1 − 𝛼 ∙ 𝑑𝑖𝑣 𝑖, ℝ ∙ 𝛽 ∙ 𝑥𝑝𝑙𝑜𝑖𝑡(𝑖, 𝕌) + 1 − 𝛽 ∙ 𝑥𝑝𝑙𝑜𝑟𝑒 𝑖, 𝕌 𝜷

25. Centre for Data Analytics XPLODIV RELEVANCE DIVERSITY EXPLOITATION EXPLORATION Average Dissimilarity Minimum Dissimilarity Dimension Instantiation Alternatives Importance of Associated Preference KNN Importance of Associated Preference User Profile Novelty Neighborhood Novelty 25 XPLODIV 𝑖, 𝕌, ℝ = 𝛼 ∙ 𝑟𝑒𝑙 𝑖 + 1 − 𝛼 ∙ 𝑑𝑖𝑣 𝑖, ℝ ∙ 𝛽 ∙ 𝑥𝑝𝑙𝑜𝑖𝑡(𝑖, 𝕌) + 1 − 𝛽 ∙ 𝑥𝑝𝑙𝑜𝑟𝑒 𝑖, 𝕌 Normalized Predicted Rating

26. Centre for Data Analytics Introduction XPLODIV Diversification Approach Conclusion and Future Work Experimental Validation Literature Review 26 Agenda

27. Centre for Data Analytics Experimental Validation Claim I XPLODIV can be tuned towards different configurations of relevance, diversity, exploitation and exploration. Claim II XPLODIV produces results comparable to baseline techniques in terms of relevance and diversity. 27

28. Centre for Data Analytics • 100,000 ratings • 943 users • 1682 movies Dataset Quantitative Tests 28 Experimental Set-Up

29. Centre for Data Analytics Baselines 29 Experimental Set-Up • No Diversity: returns the top k of candidate items. • Random Diversity: returns a random selection of k items from candidate items. • Maximal Marginal Relevance (MMR) with α=0.5 : returns k items selected with the technique MMR. • Representative of implicit diversification approaches. • Proposed by Carbonell et al. 1998. • Has served as foundation for many related more recent approaches. Quantitative Tests

32. Centre for Data Analytics No Bias • 𝛼 = 0.5, 𝛽 = 0.5. Relevance Bias • 𝛼 = 0.8, 𝛽 = 0.5. Exploitation Bias • 𝛼 = 0.2, 𝛽 = 0.7. Exploration Bias • 𝛼 = 0.2, 𝛽 = 0.3. Pure Exploitation • 𝛼 = 0.0, 𝛽 = 1.0. Pure Exploration • 𝛼 = 0.0, 𝛽 = 0.0. XPLODIV Test Cases 32 Experimental Set-Up Exploitation Bias Exploration Bias 𝜷 1.0 0.0 Relevance Bias Diversity Bias 𝜶 1.0 0.0

33. 33 Candidate Items Final Diversified Recommendation List User Profiles Item Profiles Recommendation Algorithm User-User Collaborative Filtering Apache Mahout Size 100 Matrix User - Movie Matrix Movie - Genre Traditional Recommendation Algorithm Jaccard similarity coefficient to measure similarity between Movie Items Prototype

34. Centre for Data Analytics Metrics DIVERSITY RELEVANCE Exploration Perspectives Pairwise Intra-list Dissimilarity nDCG Dissimilarity Threshold Percentage Metrics User Profile ExploitationExploitation 34 How well each item from the User Profile is represented by the set of selected items? How different are selected items from each other? How relevant are selected items considering their rank position? What is the percentage of novel items in the set of selected items?

35. 3535 Tendency Graph 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 No Diversity Random Diversity MMR XploDiv No Bias XploDiv Relevance Bias XploDiv Pure Exploitation XploDiv Exploitation Bias XploDiv Pure Exploration XploDiv Exploration Bias Relevance Diversity Exploitation Exploration

36. 3636 -3.86% -0.57% -14.56% -7.10% -13.24% -9.33% -14.19% -7.30% 137.52% 27.23% 4.78% 7.67%3.07% -0.75% 19.33% -8.17% -31.28% -27.82% 82.80% 33.25% -97.61% 37.83% 113.65% 98.86% -100% -75% -50% -25% 0% 25% 50% 75% 100% 125% 150% MMR XploDiv RelevanceBias XploDiv PureExploitation XploDiv ExploitationBias XploDiv PureExploration XploDiv ExplorationBias Relevance Diversity Exploitation Exploration Loss-Gain Graph relative to “No Diversity” Loss/GainPercentage

37. 37 Our solution: • Generates results comparable to baseline and state-of-the-art techniques. • Can be tuned towards more explorative or exploitative recommendations. Claim I Claim II Summary

38. Centre for Data Analytics Introduction XPLODIV Diversification Approach Conclusion and Future Work Experimental Validation Literature Review 38 Agenda

39. Centre for Data Analytics Conclusion 39 Contributions: 1. Analytical comparison of related work. 2. Exploitation-Exploration Diversification Technique XPLODIV. • Generates comparable results to baseline and state-of-the-art techniques. • Explicitly considers the factor of exploration. • Can be tuned to offer "exploitative diversity" or "explorative diversity" with controlled sacrifice over relevance.

40. Centre for Data Analytics Future Work • Dynamically learn values for the control parameters 𝛼 and 𝛽 to adapt XPLODIV to different user profile and dataset characteristics. • The use of XPLODIV as an aggregation strategy for results generated by different recommendation algorithms (Hybrid Recommendation Systems). • Design diversification strategies, based on XPLODIV, to enhance a Traditional Recommendation algorithm. • Example. Adapt XPLODIV to select a diverse set of neighbors in a Collaborative Filtering Recommendation System. 40

41. 41 Acknowledgements: This research was made possible by funding from Science Foundation Ireland under grant number SFI/12/RC/2289 (Insight) and by the Master's Program of the Computer Science Department at the Pontificia Universidad Javeriana, Bogotá.

XPLODIV: An Exploitation-Exploration Aware Diversification Approach for Recommender Systems

XPLODIV: An Exploitation-Exploration Aware Diversification Approach for Recommender Systems

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