Selecting a comprehensive set of reviews

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2011, Pages 168-176

  Tsaparas, Panayiotis ^a   Ntoulas, Alexandros ^b   Terzi, Evimaria ^c  

^a MICROSOFT RESEARCH   (United States)

^b Zynga   (United States)

^c BOSTON UNIVERSITY   (United States)

Author keywords

Greedy algorithms; Review selection; Set cover

Indexed keywords

APPROXIMATION ALGORITHMS;

DECISION-MAKING PROCESS; GREEDY ALGORITHMS; HIGH QUALITY; MEDICAL SERVICES; ONLINE USERS; QUALITY REVIEWS; REVIEW SELECTION; SET-COVER; USER REVIEWS; USER-GENERATED;

DECISION MAKING;

EID: 80052680844     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2020408.2020440     Document Type: Conference Paper

References (32)

1. R. Agrawal, S. Gollapudi, A. Halverson, and S. Ieong. Diversifying search results. In WSDM, pages 5-14, 2009.
2. Y. Azar, I. Gamzu, and X. Yin. Multiple intents re-ranking. In Proceedings of the 41st annual ACM symposium on Theory of computing, STOC'09, 2009.
3. A. Bhaskara, M. Charikar, E. Chlamtac, U. Feige, and A. Vijayaraghavan. Detecting high log-densities: an (1/4) approximation for densest -subgraph. In STOC, pages 201-210, 2010.
4. G. Calinescu, C. Chekuri, M. Pál, and J. Vondrák. Maximizing a submodular set function subject to a matroid constraint (extended abstract). In IPCO, pages 182-196, 2007.
5. J. G. Carbonell and J. Goldstein. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In SIGIR, pages 335-336, 1998.
6. G. Carenini, R. T. Ng, and E. Zwart. Extracting knowledge from evaluative text. In Proceedings of the 3rd international conference on Knowledge capture, K-CAP'05, pages 11-18, New York, NY, USA, 2005. ACM.
7. C. Chekuri and A. Kumar. Maximum coverage problem with group budget constraints and applications. In APPROX-RANDOM, pages 72-83, 2004.
8. H. Chen and D. R. Karger. Less is more: probabilistic models for retrieving fewer relevant documents. In SIGIR, pages 429-436, 2006.
9. C. Danescu-Niculescu-Mizil, G. Kossinets, J. Kleinberg, and L. Lee. How opinions are received by online communities: a case study on amazon.com helpfulness votes. In WWW'09, pages 141-150, New York, NY, USA, 2009. ACM.
10. A. Ghose and P. G. Ipeirotis. Designing novel review ranking systems: predicting the usefulness and impact of reviews. In ICEC'07, pages 303-310, New York, NY, USA, 2007. ACM. (Pubitemid 350229442)
11. S. Gollapudi and A. Sharma. An axiomatic approach for result diversification. In WWW, pages 381-390, 2009.
12. D. Hochbaum, editor. Approximation algorithms for NP-hard problems. PWS Publishing Company, 1997.
13. M. Hu and B. Liu. Mining and summarizing customer reviews. In KDD, pages 168-177, 2004. (Pubitemid 40114926)
14. M. Hu and B. Liu. Mining opinion features in customer reviews. In AAAI, pages 755-760, 2004.
15. S.-M. Kim, P. Pantel, T. Chklovski, and M. Pennacchiotti. Automatically assessing review helpfulness. In EMNLP, pages 423-430, Sydney, Australia, July 2006.
16. T. Lappas and D. Gunopulos. Efficient confident search in large review corpora. In ECML/PKDD (2), pages 195-210, 2010.
17. J. Liu, Y. Cao, C.-Y. Lin, Y. Huang, and M. Zhou. Low-quality product review detection in opinion summarization. In EMNLP-CoNLL, pages 334-342, 2007. Poster paper.
18. K. Liu, E. Terzi, and T. Grandison. Highlighting diverse concepts in documents. In SDM, pages 545-556, 2009.
19. Y. Liu, X. Huang, A. An, and X. Yu. Modeling and predicting the helpfulness of online reviews. In ICDM, pages 443-452, 2008.
20. Y. Lu, P. Tsaparas, A. Ntoulas, and L. Polanyi. Exploiting social context for review quality prediction. In WWW, 2010.
21. Y. Lu and C. Zhai. Opinion integration through semi-supervised topic modeling. In WWW, pages 121-130, 2008.
22. Y. Lu, C. Zhai, and N. Sundaresan. Rated aspect summarization of short comments. In WWW, pages 131-140, 2009.
23. A.-M. Popescu, B. Nguyen, and O. Etzioni. Opine: Extracting product features and opinions from reviews. In HLT/EMNLP, 2005.
24. D. Radev, T. Allison, S. Blair-Goldensohn, J. Blitzer, A. Çelebi, S. Dimitrov, E. Drabek, A. Hakim, W. Lam, D. Liu, J. Otterbacher, H. Qi, H. Saggion, S. Teufel, M. Topper, A. Winkel, and Z. Zhang. MEAD - a platform for multidocument multilingual text summarization. In LREC 2004, Lisbon, Portugal, May 2004.
25. F. Radlinski, P. N. Bennett, B. Carterette, and T. Joachims. Redundancy, diversity and interdependent document relevance. SIGIR Forum, 43(2):46-52, 2009.
26. F. Radlinski, R. Kleinberg, and T. Joachims. Learning diverse rankings with multi-armed bandits. In ICML, pages 784-791, 2008.
27. D. Rafiei, K. Bharat, and A. Shukla. Diversifying web search results. In WWW, 2010.
28. A. Slivkins, F. Radlinski, and S. Gollapudi. Learning optimally diverse rankings over large document collections. In ICML, 2010.
29. O. Tsur and A. Rappoport. Revrank: a fully unsupervised algorithm for selecting the most helpful book reviews. In ICWSM, 2009.
30. E. Vee, U. Srivastava, J. Shanmugasundaram, P. Bhat, and S. Amer-Yahia. Efficient computation of diverse query results. In ICDE, pages 228-236, 2008.
31. H. Wang, Y. Lu, and C. Zhai. Latent aspect rating analysis on review text data: a rating regression approach. In KDD, pages 783-792, 2010.
32. Z. Zhang and B. Varadarajan. Utility scoring of product reviews. In CIKM'06, pages 51-57, New York, NY, USA, 2006. ACM. (Pubitemid 47203551)