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

Soojung Lee (이수정)

doi:10.9708/jksci.2020.25.12.203

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

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2020, 25(12), pp.203-210
DOI : 10.9708/jksci.2020.25.12.203
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : October 26, 2020
Accepted : December 10, 2020
Published : December 31, 2020

Soojung Lee ¹

¹경인교육대학교

Accredited

ABSTRACT

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.

KEYWORDS

Collaborative Filtering, Recommender System, Similarity Measure, Data Sparsity

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* References for papers published after 2023 are currently being built.

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KJCKorea
Journal Central

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

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

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

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

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.