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http://dx.doi.org/10.3745/JIPS.01.0036

A Simple and Effective Combination of User-Based and Item-Based Recommendation Methods  

Oh, Se-Chang (Dept. of Computer Software, Sejong Cyber University)
Choi, Min (Dept. of Information and Communication, Chungbuk National University)
Publication Information
Journal of Information Processing Systems / v.15, no.1, 2019 , pp. 127-136 More about this Journal
Abstract
User-based and item-based approaches have been developed as the solutions of the movie recommendation problem. However, the user-based approach is faced with the problem of sparsity, and the item-based approach is faced with the problem of not reflecting users' preferences. In order to solve these problems, there is a research on the combination of the two methods using the concept of similarity. In reality, it is not free from the problem of sparsity, since it has a lot of parameters to be calculated. In this study, we propose a combining method that simplifies the combination equation of prior study. This method is relatively free from the problem of sparsity, since it has less parameters to be calculated. Thus, it can get more accurate results by reflecting the users rating to calculate the parameters. It is very fast to predict new movie ratings as well. In experiments for the proposed method, the initial error is large, but the performance gets quickly stabilized after. In addition, it showed about 6% lower average error rate than the existing method using similarity.
Keywords
Collaborative Filtering; Electronic Commerce; Recommender System; Sparsity;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 S. H. Jo, "Weight recommendation technique based on item quality to improve performance of new user recommendation and recommendation on the web," Ph.D. dissertation, Hannam University, Daejeon, Korea, 2008.
2 S. J. Lee, T. R. Jeon, G. D, Baek, and S. S. Kim, "A movie rating prediction system of user propensity analysis based on collaborative filtering and fuzzy system," Journal of Korean Institute of Intelligent Systems, vol. 19, no. 2, pp. 242-247, 2009.   DOI
3 H. C. Lee, S. J. Lee, and S. O. Kim, "A study on improvements of prediction accuracy using additional information in collaborative filtering," in Proceedings of the Korean Accounting Association 2009 Spring Conference, Seoul, Korea, 2009, pp. 349-352.
4 G. Lekakos and G. M. Giaglis, "Improving the prediction accuracy of recommendation algorithms: approaches anchored on human factors," Interacting with Computers, vol. 18, no. 3, pp. 410-431, 2006.   DOI
5 K. R. Kim, J. H. Byeon, and N. M. Moon, "Collaborative filtering design using genre similarity and preffered genre," Journal of the Korea society of Computer and Information, vol. 16, no. 4, pp. 159-168, 2011.   DOI
6 H. Ma, I. King, and M. R. Lyu, "Effective missing data prediction for collaborative filtering," in Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Amsterdam, The Netherlands, 2007, pp. 39-46.
7 P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, "GroupLens: an open architecture for collaborative filtering of netnews," in Proceeding of the 1994 ACM Conference on Computer Supported Cooperative Work, Chapel Hill, NC, 1994, pp. 175-186.
8 J. Wang, A. P. de Vries, and M. J. Reinders, "Unifying user-based and item-based collaborative filtering approaches by similarity fusion," in Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle, WA, 2006, pp. 501-508.
9 G. R. Xue, C. Lin, Q. Yang, W. Xi, H. J. Zeng, Y. Yu, and Z. Chen, "Scalable collaborative filtering using cluster-based smoothing," in Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Salvador, Brazil, 2005, pp. 114-121.
10 T. Hofmann, "Latent semantic models for collaborative filtering," ACM Transactions on Information Systems, vol. 22, no. 1, pp. 89-115, 2004.   DOI
11 GroupLens, "MovieLens datasets," [Online]. Available: http://www.grouplens.org/node/73.
12 D. M. Pennock, E. Horvitz, S. Lawrence, and C. L. Giles, "Collaborative filtering by personality diagnosis: a hybrid memory- and model-based approach," in Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, Stanford, CA, 2000, pp. 473-480.
13 J. S. Breese, D. Heckerman, and C. Kadie, "Empirical analysis of predictive algorithms for collaborative filtering," in Proceedings of the 14th conference on Uncertainty in Artificial Intelligence, Madison, WI, 1998, pp. 43-52.
14 D. S. Park, "Improved movie recommendation system based-on personal propensity and collaborative filtering," KIPS Transactions of Computer and Communication System, vol. 2, no. 11, pp. 475-482, 2013.   DOI
15 S. C, Oh and M. Choi, "Effective combination of user-based and item-based methods for movie recommendation," in Proceedings of the 2013 Korean Society of Internet Information (KSII) Fall Conference, Seoul, Korea, 2013, pp. 135-136.