Abstract
In this paper, we propose a method of ranking entities (e.g. products) based on pairwise preferences learned and inferred from user reviews. Our proposed method finds expressions from user reviews that indicate pairwise preferences of entities in terms of a certain attribute, and learns a function that determines the relative degree of the attribute to rank entities. Since there are a limited number of such expressions in reviews, we further propose a method of inferring pairwise preferences based on attribute dependencies obtained from reviews. As some pairwise preferences are less confident, we also propose a modified version of a learning to rank method, Fuzzy Ranking SVM, which can take into account the uncertainty of pairwise preferences. The experiment was carried out with three categories of products and several attributes specific to each category. The experimental results showed that our approach could learn more accurate pairwise preferences than baseline methods, and inference based on the attribute dependency could improve the performances.
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Notes
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Note that we use Japanese syntactic patterns in this paper since our experiments were conducted with Japanese reviews. English translation can be always found in the parentheses where Japanese terms are used in this paper.
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References
Bailey, P., De Vries, A.P., Craswell, N., Soboroff, I.: Overview of the TREC 2007 enterprise track. In: TREC (2007)
Balog, K., Thomas, P., Craswell, N., Soboroff, I., Bailey, P., De Vries, A.P.: Overview of the TREC 2008 enterprise track. In: TREC (2008)
Carenini, G., Ng, R.T., Zwart, E.: Extracting knowledge from evaluative text. In: K-CAP, pp. 11–18 (2005)
Chen, X., Bennett, P.N., Collins-Thompson, K., Horvitz, E.: Pairwise ranking aggregation in a crowdsourced setting. In: WSDM, pp. 193–202 (2013)
Cheng, W., Rademaker, M., De Baets, B., Hüllermeier, E.: Predicting partial orders: ranking with abstention. In: ECML-PKDD, pp. 215–230 (2010)
Choi, J., Kim, D., Kim, S., Lee, J., Lim, S., Lee, S., Kang, J.: Consento: a new framework for opinion based entity search and summarization. In: CIKM, pp. 1935–1939 (2012)
Craswell, N., de Vries, A.P., Soboroff, I.: Overview of the TREC 2005 enterprise track. In: TREC (2005)
de Vries, A.P., Vercoustre, A.-M., Thom, J.A., Craswell, N., Lalmas, M.: Overview of the INEX 2007 entity ranking track. In: Fuhr, N., Kamps, J., Lalmas, M., Trotman, A. (eds.) INEX 2007. LNCS, vol. 4862, pp. 245–251. Springer, Heidelberg (2008). doi:10.1007/978-3-540-85902-4_22
Demartini, G., Iofciu, T., de Vries, A.P.: Overview of the INEX 2009 entity ranking track. In: Geva, S., Kamps, J., Trotman, A. (eds.) INEX 2009. LNCS, vol. 6203, pp. 254–264. Springer, Heidelberg (2010). doi:10.1007/978-3-642-14556-8_26
Demartini, G., de Vries, A.P., Iofciu, T., Zhu, J.: Overview of the INEX 2008 entity ranking track. In: Geva, S., Kamps, J., Trotman, A. (eds.) INEX 2008. LNCS, vol. 5631, pp. 243–252. Springer, Heidelberg (2009). doi:10.1007/978-3-642-03761-0_25
Ganesan, K., Zhai, C.: Opinion-based entity ranking. Inf. Retrieval 15(2), 116–150 (2012)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: KDD, pp. 168–177 (2004)
Iwanari, T., Yoshinaga, N., Kaji, N., Nishina, T., Toyoda, M., Kitsuregawa, M.: Ordering concepts based on common attribute intensity. In: IJCAI, pp. 3747–3753 (2016)
Jindal, N., Liu, B.: Identifying comparative sentences in text documents. In: SIGIR, pp. 244–251 (2006)
Joachims, T.: Optimizing search engines using click through data. In: KDD, pp. 133–142 (2002)
Kim, Y., Kim, W., Shim, K.: Latent ranking analysis using pairwise comparisons. In: ICDM, pp. 869–874 (2014)
Li, S., Lin, C.Y., Song, Y.I., Li, Z.: Comparable entity mining from comparative questions. IEEE Trans. Knowl. Data Eng. 25(7), 1498–1509 (2013)
Lin, C.F., Wang, S.D.: Fuzzy support vector machines. IEEE Trans. Neural Networks 13(2), 464–471 (2002)
Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web. In: WWW, pp. 342–351 (2005)
Nelson, P.: Information and consumer behavior. J. Polit. Econ. 78(2), 311–329 (1970)
Soboroff, I., de Vries, A.P., Craswell, N.: Overview of the TREC 2006 enterprise track. In: TREC (2006)
Acknowledgments
This work was supported by JSPS KAKENHI Grant Numbers JP15H01718, JP26700009, JP16H02906, JP16K16156, and JP25240050.
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Uchida, S., Yamamoto, T., Kato, M.P., Ohshima, H., Tanaka, K. (2017). Entity Ranking by Learning and Inferring Pairwise Preferences from User Reviews. In: Sung, WK., et al. Information Retrieval Technology. AIRS 2017. Lecture Notes in Computer Science(), vol 10648. Springer, Cham. https://doi.org/10.1007/978-3-319-70145-5_11
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