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A Recommender System Fusing Collaborative Filtering and User’s Review Mining

Authors: Hyunchul Ahn, Seulbi Choi


Collaborative filtering (CF) algorithm has been popularly used for recommender systems in both academic and practical applications. It basically generates recommendation results using users’ numeric ratings. However, the additional use of the information other than user ratings may lead to better accuracy of CF. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's review can be regarded as the new informative source for identifying user's preference with accuracy. Under this background, this study presents a hybrid recommender system that fuses CF and user's review mining. Our system adopts conventional memory-based CF, but it is designed to use both user’s numeric ratings and his/her text reviews on the items when calculating similarities between users.

Keywords: Text Mining, Recommender System, Collaborative Filtering, Review mining

Digital Object Identifier (DOI):

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[1] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-Based Collaborative Filtering Recommendation Algorithms,” in Proc. of the 10th international conference on World Wide Web, pp. 285-295, 2001.
[2] K.-j. Kim, and Y. Kim, “Recommender System using Implicit Trust-enhanced Collaborative Filtering,” Journal of Intelligence and Information Systems, vol. 19, no. 4, pp. 1-10, 2013.
[3] Z. Zhang, D. Zhang, and J. Lai, “urCF: User Review Enhanced Collaborative Filtering,” in Proc. of 20th Americas Conference on Information Systems, Savannah, pp. 1-11, 2014.
[4] B. Jeon, and H. Ahn, “A Collaborative Filtering System Combined with Users’ Review Mining: Application to the Recommendation of Smartphone Apps.” Journal of Intelligence and Information Systems, vol. 21, no. 2, pp. 1-18, 2015.
[5] S. Dhanasobhon, P.-y. Chen, and M. D. Smith, “An Analysis of the Differential Impact of Reviews and Reviewers at,” in Proc. of International Conference on Information Systems, pp. 1-17, 2007.
[6] K.-j. Kim, and H. Ahn, “Collaborative Filtering with a User-Item Matrix Reduction Technique,” International Journal of Electronic Commerce, vol. 16, no.1, pp. 107-128, 2011.
[7] X. Yang, Y. Guo, Y. Liu, and H. Steck, “A survey of collaborative filtering based social recommender systems,” Computer Communications, vol. 41, pp. 1-10, 2014.
[8] M. Balabanovic and Y. Shoham, “Fab: Content-based, collaborative recommendation,” Communications of the ACM, vol. 40, no. 3, pp. 66-72, 1997.
[9] D. Billsus and M.J. Pazzani, “Learning Collaborative Information Filters,” in Proc. of the 15th International conference on Machine Learning, pp. 46-54, 1998.
[10] Y.H. Cho and J.K. Kim, “Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce,” Expert Systems with Applications, vol. 23, no. 2, pp. 233-246, 2004.
[11] Y.H. Cho, J.K. Kim, and S.H. Kim, “A personalized recommender system based on Web usage mining and decision tree induction,” Expert Systems with Application, vol. 23, no. 3, pp. 329-342, 2002.
[12] J.S. Breese, D. Heckerman, and C. Kadie, “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” in Proc. of 14th Conference on Uncertainty in Artificial Intelligence, pp, 43-52, 1998.
[13] J.B. Schafer, J. Konstan, and J. Riedl, “Electronic commerce recommender applications,” Journal of Data Mining and Knowledge Discovery, vol. 5, no. 1-2, pp. 115-152, 2001.
[14] I.H. Witten, Text Mining, 2005.
[15] W. Fan, L. Wallace, S. Rich, and Z. Zhang, “Tapping the power of text mining,” Communications of the ACM, vol. 49, no. 9, pp. 76-82, 2006.