Abstract
With booming electronic commerce, online reviews are often created by users like who buys a product or goes to a restaurant. However, littery and unordered free-text reviews make it difficult for new users to acquire and analyze useful information. Thus, recommendation system plays an increasingly important role in online surfing. Nowadays, it has been proved that recommendation system based on topics is an available method in the theory and practice. However, there is little study to extract preferences from the perspective of sentiment. The method we proposed is to combine the topics and sentiments for generating a user’s preference from the user’s previous reviews. According to the degree of similarity with public’s preference, recommendation system we proposed would judge whether it should recommend the new products to this user. The empirical results show that the recommendation system we proposed can make accurately and effectively recommend.
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References
Lee, K., Lee, K.: Escaping your comfort zone: A graph-based recommender system for finding novel recommendations among relevant items. Expert Syst. Appl. 42(10), 4851–4858 (2015)
Li, Y.-M., Chun-Te, W., Lai, C.-Y.: A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship. Decisi. Support Syst. 55(3), 740–752 (2013)
Dooms, S., Audenaert, P., Fostier, J., De Pessemier, T., Martens, L.: In-memory, distributed content-based recommender system. J. Intel. Inform. Syst. 42(3), 645–669 (2014)
Huang, Z., Zeng, D., Chen, H.: A comparison of collaborative filtering recommendation algorithms for e-commerce. IEEE Intel. Syst. 22(5), 68–78 (2007)
Castro-Sanchez, J.J., Miguel, R., Vallejo, D., López-López, L.M.: A highly adaptive recommender system based on fuzzy logic for B2C e-commerce portals. Expert Syst. Appl. 38(3), 2441–2454 (2011)
Bobadilla, J., Serradilla, F., Hernando, A.: Collaborative filtering adapted to recommender systems of e-learning. Knowl. Based Syst. 22, 261–265 (2009)
Lee, S.K., Cho, Y.H., Kim, S.H.: Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations. Inform. Sci. 180(11), 2142–2155 (2010)
Tan, S., Bu, J., Chen, C.H., He, X.: Using rich social media information for music recommendation via hypergraph model. ACM Trans. Multimedia Comput., Commun. Appl. 7(1), Article 7 (2011)
Núñez-Valdéz, E.R., Cueva-Lovelle, J.M., Sanjuán-Martínez, O., García-Díaz, V., Ordoñez, P., Montenegro-Marín, C.E.: Implicit feedback techniques on recommender systems applied to electronic books. Comput. Hum. Behav. 28(4), 1186–1193 (2012)
Barragáns-Martínez, A.B., Costa-Montenegro, E., Burguillo, J.C., Rey-López, M., Mikic-Fonte, F.A., Peleteiro, A.: A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition. Inform. Sci. 180(22), 4290–4311 (2010)
Mcnally, K., O’mahony, M.P., Coyle, M., Briggs, P., Smyth, B.: A case study of collaboration and reputation in social web search, ACM Trans. Intel. Syst. Technol. 3(1), Article 4 (2011)
Christidis, K., Mentzas, G.: A topic-based recommender system for electronic marketplace platforms. Expert Syst. Appl. 40, 4370–4379 (2013)
Li, X., Murata, T.: Customizing knowledge-based recommender system by tracking analysis of user behavior. In: Proceedings of the IEEE 17th International Conference Industrial Engineering and Engineering Management (IE&EM), pp. 65–69 (2010)
Krishna, P.V., Misra, S., Joshi, D., Obaidat, M.S.: Learning automata based sentiment analysis for recommender system on cloud. In: 2013 International Conference on Computer, Information and Telecommunication Systems (CITS), pp. 1–5. IEEE, May 2013
Park, M.K., Moon, N.: The Effects of personal sentiments and contexts on the acceptance of music recommender systems. In: 2011 5th FTRA International Conference on Multimedia and Ubiquitous Engineering (MUE), pp. 289–292. IEEE, June 2011
Tian, P., Zhu, Z., Xiong, L., Xu, F.: A recommendation mechanism for web publishing based on sentiment analysis of microblog, wuhan university. J. Nat. Sci. 22(2), 146–152 (2015)
Leung, C.W., Chan, S.C., Chung, F.L.:. Integrating collaborative filtering and sentiment analysis: A rating inference approach. In: Proceedings of the ECAI 2006 Workshop on Recommender Systems, pp. 62–66, August 2006
Meyffret, S., Guillot, E., Medini, L., Laforest, F.: RED: A Rich Epinions Dataset for Recommender Systems. Université de Lyon (2012)
Ganu, G., Elhadad, N., Marian, A.: Beyond the stars: Improving rating predictions using review text content. In: Proceedings of the 12th International Workshop on the Web and Databases (2009)
Acknowledgements
This work was supported by the Fundamental Research Funds for the Central Universities (WUT:2014-IV-054).
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Cao, N., Cao, J., Liu, P., Li, W. (2015). Sentimental Preference Extraction from Online Reviews for Recommendation. In: Di Fatta, G., Fortino, G., Li, W., Pathan, M., Stahl, F., Guerrieri, A. (eds) Internet and Distributed Computing Systems. IDCS 2015. Lecture Notes in Computer Science(), vol 9258. Springer, Cham. https://doi.org/10.1007/978-3-319-23237-9_26
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DOI: https://doi.org/10.1007/978-3-319-23237-9_26
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