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http://dx.doi.org/10.7236/JIIBC.2021.21.4.183

A Stepwise Rating Prediction Method for Recommender Systems  

Lee, Soojung (Dept. of Computer Education, Gyeongin National University of Education)
Publication Information
The Journal of the Institute of Internet, Broadcasting and Communication / v.21, no.4, 2021 , pp. 183-188 More about this Journal
Abstract
Collaborative filtering based recommender systems are currently indispensable function of commercial systems in various fields, being a useful service by providing customized products that users will prefer. However, there is a high possibility that the prediction of preferrable products is inaccurate, when the user's rating data are insufficient. In order to overcome this drawback, this study suggests a stepwise method for prediction of product ratings. If the application conditions of the prediction method corresponding to each step are not satisfied, the method of the next step is applied. To evaluate the performance of the proposed method, experiments using a public dataset are conducted. As a result, our method significantly improves prediction and precision performance of collaborative filtering systems employing various conventional similarity measures and outperforms performance of the previous methods for solving rating data sparsity.
Keywords
Collaborative filtering; Data sparsity; Rating prediction; Recommender system;
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