Diversifying product review rankings: Getting the full picture

Proceedings - 2011 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2011

Volumn 1, Issue , 2011, Pages 138-145

  Krestel, Ralf ^a   Dokoohaki, Nima ^b  

^a L3S RESEARCH CENTER   (Germany)

^b ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

Author keywords

Diversification; Ranking; Review recommendation; Summarization; Topic models

Indexed keywords

DIVERSIFICATION; LATENT DIRICHLET ALLOCATION; PRODUCT DESCRIPTIONS; PRODUCT REVIEWS; RANKING; RANKING STRATEGY; SUMMARIZATION; TOPIC MODEL;

USER INTERFACES;

EID: 80155182260     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WI-IAT.2011.33     Document Type: Conference Paper

References (43)

1. J. a. Chevalier and D. Mayzlin, "The effect of word of mouth on sales: Online book reviews," Journal of Marketing Research, vol. 43, no. 3, pp. 345-354, 2006.
2. M. P. O'Mahony and B. Smyth, "Learning to recommend helpful hotel reviews," Proceedings of the third ACM conference on Recommender systems - RecSys '09, p. 305, 2009.
3. S. Aciar, D. Zhang, S. Simoff, and J. Debenham, "Informed recommender: Basing recommendations on consumer product reviews," Online, no. June, pp. 39-47, 2007.
4. A. Ghose and P. G. Ipeirotis, "Designing novel review ranking systems: predicting the usefulness and impact of reviews," in Proceedings of the ninth international conference on Electronic commerce, ser. ICEC '07. New York, NY, USA: ACM, 2007, pp. 303-310.
5. N. Tintarev and J. Masthoff, "A survey of explanations in recommender systems," in Data Engineering Workshop. IEEE, 2007, pp. 801-810.
6. -, "The effectiveness of personalized movie explanations: An experiment using commercial meta-data," in Adaptive Hypermedia and Adaptive Web-Based Systems. Springer, 2008, pp. 204-213.
7. M. O'Mahony, P. Cunningham, and B. Smyth, "An assessment of machine learning techniques for review recommendation," Science, 2009.
8. S. Kim, P. Pantel, T. Chklovski, and M. Pennacchiotti, "Automatically assessing review helpfulness," in Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2006, pp. 423-430.
9. Y. Liu, X. Huang, A. An, and X. Yu, "Modeling and predicting the helpfulness of online reviews," in ICDM'08. IEEE, 2009, pp. 443- 452.
10. F. Harper, D. Moy, and J. Konstan, "Facts or friends?: distinguishing informational and conversational questions in social q&a sites," in Proceedings of the 27th international conference on Human factors in computing systems. ACM, 2009, pp. 759-768.
11. W. Weerkamp and M. De Rijke, "Credibility improves topical blog post retrieval," ACL-08: HLT, pp. 923-931, 2008.
12. J. Wiebe and E. Riloff, Creating subjective and objective sentence classifiers from unannotated texts, ser. Lecture Notes in Computer Science. Springer, 2005, vol. pages, no. 34063406, pp. 486-497.
13. P. Chaovalit and L. Zhou, "Movie review mining: a comparison between supervised and unsupervised classification approaches," Proceedings of the 38th Annual Hawaii International Conference on System Sciences, vol. 00, no. C, pp. 112c-112c, 2005.
14. K. Sparck Jones, "What might be in a summary," Information Retrieval 93 Von der Modellierung zur Anwendung, pp. 9-26, 1993.
15. G. Salton, A. Singhal, C. Buckley, and M. Mitra, "Automatic text decomposition using text segments and text themes," Proceedings of the the seventh ACM conference on Hypertext HYPERTEXT 96, pp. 53-65, 1996.
16. L. Zhuang, F. Jing, and X.-Y. Zhu, "Movie review mining and summarization," Proceedings of the 15th ACM international conference on Information and knowledge management - CIKM '06, p. 43, 2006.
17. P. Beineke, T. Hastie, C. Manning, and S. Vaithyanathan, "An exploration of sentiment summarization," in Proc. of AAAI. Citeseer, 2003, pp. 12-15.
18. M. Hu and B. Liu, "Mining and summarizing customer reviews," in Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2004, pp. 168-177.
19. A. Popescu and O. Etzioni, "Extracting product features and opinions from reviews," in Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2005, pp. 339-346.
20. M. Gamon, A. Aue, S. Corston-Oliver, and E. Ringger, "Pulse: Mining customer opinions from free text," Advances in Intelligent Data Analysis VI, pp. 121-132, 2005.
21. 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 SIGIR 98, vol. pp, pp. 335-336, 1998.
22. 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 WWW 05, p. 22, 2005.
23. F. Radlinski and S. Dumais, "Improving personalized web search using result diversification," Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval SIGIR 06, pp. 691-692, 2006.
24. H. Chen and D. R. Karger, Less is more: probabilistic models for retrieving fewer relevant documents, ser. SIGIR '06. ACM, 2006, pp. 429-436.
25. C. Zhai, W. W. Cohen, and J. Lafferty, Beyond independent relevance: Methods and evaluation metrics for subtopic retrieval. ACM, 2003.
26. M. Sanderson, J. Tang, T. Arni, and P. Clough, "What else is there? search diversity examined," Advances in Information Retrieval, vol. 5478, p. 562569, 2009.
27. S. Gollapudi and A. Sharma, "An axiomatic approach for result diversification," in Proceedings of the 18th international conference on World wide web WWW 09, ser. WWW '09. ACM Press, 2009, p. 381.
28. J. Wang and J. Zhu, "Portfolio theory of information retrieval," Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval SIGIR 09, p. 115, 2009.
29. D. Rafiei, K. Bharat, and A. Shukla, Diversifying web search results. ACM Press, 2010, p. 781.
30. R. Agrawal, S. Gollapudi, A. Halverson, and S. Ieong, "Diversifying search results," Proceedings of the Second ACM International Conference on Web Search and Data Mining WSDM 09, p. 5, 2009.
31. E. Vee, U. Srivastava, J. Shanmugasundaram, P. Bhat, and S. A. Yahia, "Efficient computation of diverse query results," vol. 00, pp. 228-236, 2008.
32. D. Schnitzer, A. Flexer, and G. Widmer, "A filter-and-refine indexing method for fast similarity search in millions of music tracks," in Proceedings of the 10th International Conference on Music Information Retrieval (ISMIR09), 2009.
33. K. Toutanova, D. Klein, C. D. Manning, and Y. Singer, "Featurerich part-of-speech tagging with a cyclic dependency network," in In Proceedings of HLT-NAACL 2003, 2003, pp. 252-259.
34. C. Fellbaum, Ed., WordNet An Electronic Lexical Database. Cambridge, MA ; London: The MIT Press, May 1998.
35. S. Deligne and F. Bimbot, "Language modeling by variable length sequences: theoretical formulation and evaluation of multigrams," Acoustics, Speech, and Signal Processing, IEEE International Conference on, vol. 1, pp. 169-172, 1995.
36. F. Bimbot, R. Pieraccini, E. Levin, and B. Atal, "Variable-length sequence modeling: multigrams," Signal Processing Letters, IEEE, vol. 2, no. 6, pp. 111 -113, June 1995.
37. M. Steyvers and T. Griffiths, Probabilistic Topic Models. Lawrence Erlbaum Associates, 2007.
38. J. Bross and H. Ehrig, "Generating a context-aware sentiment lexicon for aspect-based product review mining," in WI-IAT '10: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. Washington, DC, USA: IEEE Computer Society, August 31-September 3 2010.
39. D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation," Journal of Machine Learning Research, vol. 3, pp. 993-1022, January 2003.
40. A. K. McCallum, "MALLET: A machine learning for language toolkit," 2002, http://mallet.cs.umass.edu.
41. C. L. A. Clarke, N. Craswell, and I. Soboroff, "Overview of the TREC 2009 web track," in Proc. of TREC-2009.
42. 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," in Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, ser. SIGIR '08. New York, NY, USA: ACM, 2008, pp. 659- 666.
43. R. Arun, V. Suresh, C. Veni Madhavan, and M. Narasimha Murthy, "On finding the natural number of topics with latent dirichlet allocation: Some observations," in Advances in Knowledge Discovery and Data Mining: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings, ser. Lecture Notes in Computer Science, M. Zaki, J. Yu, B. Ravindran, and V. Pudi, Eds., vol. 6118. Springer Berlin / Heidelberg, 2010, pp. 391-402.