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http://dx.doi.org/10.9708/jksci.2022.27.09.149

Time-aware Collaborative Filtering with User- and Item-based Similarity Integration  

Lee, Soojung (Dept. of Computer Education, Gyeongin National University of Education)
Publication Information
Journal of the Korea Society of Computer and Information / v.27, no.9, 2022 , pp. 149-155 More about this Journal
Abstract
The popularity of e-commerce systems on the Internet is increasing day by day, and the recommendation system, as a core function of these systems, greatly reduces the effort to search for desired products by recommending products that customers may prefer. The collaborative filtering technique is a recommendation algorithm that has been successfully implemented in many commercial systems, but despite its popularity and usefulness in academia, the memory-based implementation has inaccuracies in its reference neighbor. To solve this problem, this study proposes a new time-aware collaborative filtering technique that integrates and utilizes the neighbors of each item and each user, weighting the recent similarity more than the past similarity with them, and reflecting it in the recommendation list decision. Through the experimental evaluation, it was confirmed that the proposed method showed superior performance in terms of prediction accuracy than other existing methods.
Keywords
Collaborative Filtering; Memory-based Collaborative Filtering; Time-aware Recommender System; Similarity Measure;
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Times Cited By KSCI : 2  (Citation Analysis)
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