Abstract
Item reviews are a valuable source of information for potential buyers, who are looking for information on a product’s attributes before making a purchase decision. This search of information is often hindered by overwhelming numbers of available reviews, as well as low-quality and noisy content. While a significant amount of research has been devoted to filtering and organizing review corpora toward the benefit of the buyers, a crucial part of the reviewing process has been overlooked: reviewer satisfaction. As in every content-based system, the content-generators, in this case the reviewers, serve as the driving force. Therefore, keeping the reviewers satisfied and motivated to continue submitting high-quality content is essential. In this paper, we propose a system that helps potential buyers by focusing on high-quality and informative reviews, while keeping reviewers content and motivated.
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References
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Rec. 22, 207–216 (1993)
Bailey, J., Manoukian, T., Ramamohanarao, K.: A fast algorithm for computing hypergraph transversals and its application in mining emerging patterns. In: ICDM, pp. 485–488 (2003)
Eiter, T., Gottlob, G.: Identifying the minimal transversals of a hypergraph and related problems. SIAM J. Comput. 24, 1278–1304 (1995)
Ghani, R., Probst, K., Liu, Y., Krema, M., Fano, A.: Text mining for product attribute extraction. SIGKDD Explorations Newsletter (2006)
Hébert, C., Bretto, A., Crémilleux, B.: A data mining formalization to improve hypergraph minimal transversal computation. Fundam. Inf. 80, 415–433 (2007)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: SIGKDD (2004)
Hu, M., Liu, B.: Mining opinion features in customer reviews. In: AAAI (2004)
Jindal, N., Liu, B.: Opinion spam and analysis. In: WSDM 2008 (2008)
Kincaid, J., Fishburne, R., Rogers, R., Chissom, B.: Derivation of new readability formulas for navy enlisted personnel. In: Research Branch Report 8-75. Naval Technical Training, Millington (1975)
Ku, L.-W., Liang, Y.-T., Chen, H.-H.: Opinion extraction, summarization and tracking in news and blog corpora. In: AAAI-CAAW (2006)
Lappas, T., Gunopulos, D.: Efficient confident search in large review corpora. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS, vol. 6322, pp. 195–210. Springer, Heidelberg (2010)
Liu, J., Cao, Y., Lin, C.-Y., Huang, Y., Zhou, M.: Low-quality product review detection in opinion summarization. In: EMNLP-CoNLL (2007)
Lu, Y., Tsaparas, P., Ntoulas, A., Polanyi, L.: Exploiting social context for review quality prediction. In: WWW 2010 (2010)
Min Kim, S., Pantel, P., Chklovski, T., Pennacchiotti, M.: Automatically assessing review helpfulness. In: EMNLP 2006 (2006)
Motwani, R., Raghavan, P.: Randomized Algorithms. Cambridge University Press, Cambridge (1995)
Popescu, A.-M., Etzioni, O.: Extracting product features and opinions from reviews. In: HLT 2005 (2005)
Riloff, E., Patwardhan, S., Wiebe, J.: Feature subsumption for opinion analysis. In: EMNLP (2006)
Li, M.X., Tan, C.H., Wei, K.K., Wang, K.L.: Where to place product review? an information search process perspective. In: ICIS (2010)
Yilmaz, E., Aslam, J.A., Robertson, S.: A new rank correlation coefficient for information retrieval. In: SIGIR, pp. 587–594 (2008)
Zhang, Z., Varadarajan, B.: Utility scoring of product reviews. In: CIKM (2006)
Zhuang, L., Jing, F., Zhu, X., Zhang, L.: Movie review mining and summarization. In: CIKM (2006)
Tsaparas, P., Ntoulas, A., Terzi.: Constructing a comprehensive set of reviews. In: SIGKDD (2011)
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Lappas, T., Terzi, E. (2011). Toward a Fair Review-Management System. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2011. Lecture Notes in Computer Science(), vol 6912. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23783-6_19
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DOI: https://doi.org/10.1007/978-3-642-23783-6_19
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