A Simple and Effective Combination of User-Based and Item-Based Recommendation Methods
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초록
User-based and item-based approaches have been developed as the solutions of the movie recommendationproblem. However, the user-based approach is faced with the problem of sparsity, and the item-based approachis faced with the problem of not reflecting users’ preferences. In order to solve these problems, there is a researchon the combination of the two methods using the concept of similarity. In reality, it is not free from the problemof sparsity, since it has a lot of parameters to be calculated. In this study, we propose a combining method thatsimplifies 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 ratingto calculate the parameters. It is very fast to predict new movie ratings as well. In experiments for the proposedmethod, the initial error is large, but the performance gets quickly stabilized after. In addition, it showed about6% lower average error rate than the existing method using similarity.
Abstract
User-based and item-based approaches have been developed as the solutions of the movie recommendationproblem. However, the user-based approach is faced with the problem of sparsity, and the item-based approachis faced with the problem of not reflecting users’ preferences. In order to solve these problems, there is a researchon the combination of the two methods using the concept of similarity. In reality, it is not free from the problemof sparsity, since it has a lot of parameters to be calculated. In this study, we propose a combining method thatsimplifies 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 ratingto calculate the parameters. It is very fast to predict new movie ratings as well. In experiments for the proposedmethod, the initial error is large, but the performance gets quickly stabilized after. In addition, it showed about6% lower average error rate than the existing method using similarity.
참고문헌 (15)
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