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

Se-Chang Oh and Min Choi
Volume: 15, No: 1, Page: 127 ~ 136, Year: 2019
10.3745/JIPS.01.0036
Keywords: Collaborative Filtering, Electronic Commerce, Recommender System, Sparsity
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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.

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Cite this article
IEEE Style
S. O. M. Choi, "A Simple and Effective Combination of User-Based and Item-Based Recommendation Methods," Journal of Information Processing Systems, vol. 15, no. 1, pp. 127~136, 2019. DOI: 10.3745/JIPS.01.0036.

ACM Style
Se-Chang Oh and Min Choi. 2019. A Simple and Effective Combination of User-Based and Item-Based Recommendation Methods, Journal of Information Processing Systems, 15, 1, (2019), 127~136. DOI: 10.3745/JIPS.01.0036.