An axiomatic approach for result diversification

WWW'09 - Proceedings of the 18th International World Wide Web Conference

Volumn , Issue , 2009, Pages 381-390

  Gollapudi, Sreenivas ^a   Sharma, Aneesh ^b  

^a MICROSOFT RESEARCH   (United States)

^b Stanford University   (United States)

Author keywords

Approximation algorithms; Axiomatic framework; Diversification; Facility dispersion; Search engine; Wikipedia

Indexed keywords

AXIOMATIC APPROACH; AXIOMATIC FRAMEWORK; CLUSTERING SYSTEM; DATA SETS; DIVERSIFICATION; EVALUATION METHODOLOGIES; EXPLICIT KNOWLEDGE; PRODUCT DATABASE; RANKING SYSTEM; USER SATISFACTION; WIKIPEDIA;

APPROXIMATION ALGORITHMS; DISPERSIONS; WEBSITES;

SEARCH ENGINES;

EID: 84865627427     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1526709.1526761     Document Type: Conference Paper

References (23)

1. R. Agrawal, S. Gollapudi, A. Halverson, and S. Ieong. Diversifying search results. In Proc. 2nd ACM Intl Conf on Web Search and Data Mining, 2009.
2. A. Altman and M. Tennenholtz. On the axiomatic foundations of ranking systems. In Proc. 19th International Joint Conference on Artificial Intelligence, pages 917-922, 2005.
3. Kenneth Arrow. Social Choice and Individual Values. Wiley, New York, 1951.
4. Yair Bartal. On approximating arbitrary metrices by tree metrics. In STOC, pages 161-168, 1998.
5. Andrei Z. Broder, Moses Charikar, Alan M. Frieze, and Michael Mitzenmacher. Min-wise independent permutations. Journal of Computer and System Sciences, 60(3):630-659, 2000.
6. J. Carbonell and J. Goldstein. The use of MMR, diversity-based reranking for reordering documents and producing summaries. Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, pages 335-336, 1998.
7. Barun Chandra and Magnús M. Halldórsson. Approximation algorithms for dispersion problems. J. Algorithms, 38(2):438-465, 2001.
8. H. Chen and D.R. Karger. Less is more: probabilistic models for retrieving fewer relevant documents. Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 429-436, 2006.
9. C.L.A. Clarke, M. Kolla, G.V. Cormack, O. Vechtomova, A. Ashkan, S. Büttcher, and I. MacKinnon. Novelty and diversity in information retrieval evaluation. Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pages 659-666, 2008.
10. Sreenivas Gollapudi and Rina Panigrahy. Exploiting asymmetry in hierarchical topic extraction. In CIKM, pages 475-482, 2006.
11. R. Hassin, S. Rubinstein, and A. Tamir. Approximation algorithms for maximum dispersion. Operations Research Letters, 21(3):133-137, 1997.
12. J. Kleinberg. An Impossibility Theorem for Clustering. Advances in Neural Information Processing Systems 15: Proceedings of the 2002 Conference, 2003.
13. B. Korte and D. Hausmann. An Analysis of the Greedy Heuristic for Independence Systems. Algorithmic Aspects of Combinatorics, 2:65-74, 1978.
14. SS Ravi, D.J. Rosenkrantz, and G.K. Tayi. Facility dispersion problems: Heuristics and special cases. Proc. 2nd Workshop on Algorithms and Data Structures (WADS), pages 355-366, 1991.
15. S.S. Ravi, D.J. Rosenkrantz, and G.K. Tayi. Heuristic and special case algorithms for dispersion problems. Operations Research, 42(2):299-310, 1994.
16. SS Ravi, D.J. Rosenkrantzt, and G.K. Tayi. Approximation Algorithms for Facility Dispersion. In Teofflo F. Gonzalez, editor, Handbook of Approximation Algorithms and Metaheuristics. Chapman & Hall/CRC, 2007.
17. Stephen Robertson and Hugo Zaragoza. On rank-based effectiveness measures and optimization. Inf. Retr., 10(3):321-339, 2007.
18. Atish Das Sarma, Sreenivas Gollapudi, and Samuel Ieong. Bypass rates: reducing query abandonment using negative inferences. In KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 177-185, New York, NY, USA, 2008. ACM.
19. E. Vee, U. Srivastava, J. Shanmugasundaram, P. Bhat, and S.A. Yahia. Efficient Computation of Diverse Query Results. IEEE 24th International Conference on Data Engineering, 2008. ICDE 2008, pages 228-236, 2008.
20. ChengXiang Zhai. Risk Minimization and Language Modeling in Information Retrieval. PhD thesis, Carnegie Mellon University, 2002.
21. C.X. Zhai, W.W. Cohen, and J. Lafferty. Beyond independent relevance: methods and evaluation metrics for subtopic retrieval. Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 10-17, 2003.
22. C.X. Zhai and J. Lafferty. A risk minimization framework for information retrieval. Information Processing and Management, 42(1):31-55, 2006.
23. C.N. Ziegler, S.M. McNee, J.A. Konstan, and G. Lausen. Improving recommendation lists through topic diversification. Proceedings of the 14th international conference on World Wide Web, pages 22-32, 2005.