Similarity Measure based on Utilization of Rating Distributions for Data Sparsity Problem in Collaborative Filtering

• Published in: 한국컴퓨터정보학회논문지 Vol. 25; no. 12; pp. 203 - 210
• Main Author: Soojung Lee(이수정)
• Format: Journal Article
• Language: English
• Published: 한국컴퓨터정보학회 01.12.2020
• Subjects: 컴퓨터학; Data Sparsity; Collaborative Filtering; 추천 시스템; 협력 필터링; Recommender System; 유사도 척도; Similarity Measure; 데이터 희소성
• ISSN: 1598-849X; 2383-9945
• DOI: 10.9708/jksci.2020.25.12.203

Memory-based collaborative filtering is one of the representative types of the recommender system, but it suffers from the inherent problem of data sparsity. Although many works have been devoted to solving this problem, there is still a request for more systematic approaches to the problem. This study exploits distribution of user ratings given to items for computing similarity. All user ratings are utilized in the proposed method, compared to previous ones which use ratings for only common items between users. Moreover, for similarity computation, it takes a global view of ratings for items by reflecting other users’ ratings for that item. Performance is evaluated through experiments and compared to that of other relevant methods. The results reveal that the proposed demonstrates superior performance in prediction and rank accuracies. This improvement in prediction accuracy is as high as 2.6 times more than that achieved by the state-of-the-art method over the traditional similarity measures. KCI Citation Count: 0