Preferential Diversity

ABSTRACT

The ever increasing supply of data is bringing a renewed attention to query personalization. Query personalization is a technique that utilizes user preferences with the goal of providing relevant results to the users. Along with preferences, diversity is another important aspect of query personalization especially useful during data exploration. The goal of result diversification is to reduce the amount of redundant information included in the results. Most previous approaches of result diversification focus solely on generating the most diverse results, which do not take user preferences into account. In this paper, we propose a novel framework called Preferential Diversity (PrefDiv) that aims to support both relevancy and diversity of user query results. PrefDiv utilizes user preference models that return ranked results and reduces the redundancy of results in an efficient and flexible way. PrefDiv maintains the balance between relevancy and diversity of the query results by providing users with the ability to control the trade-off between the two. We describe an implementation of PrefDiv on top of the HYPRE preference model, which allows users to specify both qualitative and quantitative preferences and unifies them using the concept of preference intensities. We experimentally evaluate its performance by comparing with state-of-the-art diversification techniques; our results indicate that PrefDiv achieves significantly better balance between diversity and relevance.

References

1. R. Agrawal, S. Gollapudi, A. Halverson, and S. Ieong. Diversifying search results. In WSDM, pages 5--14, 2009.
2. A. Angel and N. Koudas. Efficient diversity-aware search. In SIGMOD, pages 781--792, 2011.
3. A. Borodin, H. C. Lee, and Y. Ye. Max-sum diversification, monotone submodular functions and dynamic updates. In PODS, pages 155--166, 2012.
4. C. L. Clarke, M. Kolla, G. V. Cormack, O. Vechtomova, A. Ashkan, S. Bãijttche, and I. MacKinnon. Novelty and diversity in information retrieval evaluation. In SIGIR, pages 659--666, 2008.
5. M. Drosou and E. Pitoura. Search result diversification. ACM SIGMOD Record, 39(1):41--47, 2010.
6. M. Drosou and E. Pitoura. Result diversification based on dissimilarity and coverage. In VLDB, pages 13--24, 2012.
7. P. Fraternali, D. Martinenghi, and M. Tagliasacchi. Top-k bounded diversification. In SIGMOD, pages 421--432, 2012.
8. R. Gheorghiu. Unifying Qualitative and Quantitative Database Preferences to Enhance Query Personalization. PhD thesis, University of Pittsburgh, Sep 2014.
9. R. Gheorghiu, A. Labrinidis, and P. K. Chrysanthis. A user-friendly framework for database preferences. In CollaborativeCom, pages 205--214, 2014.
10. S. Gollapudi and A. Sharma. An axiomatic approach for result diversification. In WWW, pages 381--390, 2009.
11. H. V. Jagadish, J. Gehrke, A. Labrinidis, Y. Papakonstantinou, J. M. Patel, R. Ramakrishnan, and C. Shahabi. Big data and its technical challenges. Commun. ACM, 57(7):86--94, July 2014.
12. W. Kiessling and G. Köstler. Preference SQL: design, implementation, experiences. In VLDB, pages 990--1001, 2002.
13. A. Labrinidis. The big data - same humans problem. In Proc. of Conference of Innovative Data Systems Research, 2015.
14. K. Stefanidis, G. Koutrika, and E. Pitoura. A survey on representation, composition and application of preferences in database systems. ACM TODS, 36(19), 2011.
15. J. Tang, J. Zhang, R. Jin, Z. Yang, K. Cai, L. Zhang, and Z. Su. Topic level expertise search over heterogeneous networks. Machine Learning J., 82(2):211--237, 2011.
16. M. R. Vieira, H. L. Razente, M. C. N. Barioni, M. Hadjieleftheriou, D. Srivastava, C. Traina, and V. J. Tsotras. On query result diversification. In ICDE, pages 1163--1174, 2011.
17. C.-N. Ziegler, J. A. K. Sean M. McNee, and G. Lausen. Improving recommendation lists through topic diversification. In WWW, pages 22--32, 2005.