Using User Rating Patterns for Selecting Neighbors in Collaborative Filtering (협력필터링에서 이웃 선정을 위한 사용자 평가 패턴의 활용)

Soojung Lee (이수정)

doi:10.9708/jksci.2019.24.09.077

Using User Rating Patterns for Selecting Neighbors in Collaborative Filtering

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2019, 24(9), pp.77-82
DOI : 10.9708/jksci.2019.24.09.077
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : July 29, 2019
Accepted : September 3, 2019
Published : September 30, 2019

Soojung Lee ¹

¹경인교육대학교

Accredited

ABSTRACT

Collaborative filtering is a popular technique for recommender systems and used in many practical commercial systems. Its basic principle is select similar neighbors of a current user and from their past preference information on items the system makes recommendations for the current user. One of the major problems inherent in this type of system is data sparsity of ratings. This is mainly caused from the underlying similarity measures which produce neighbors based on the ratings records. This paper handles this problem and suggests a new similarity measure. The proposed method takes users rating patterns into account for computing similarity, without just relying on the commonly rated items as in previous measures. Performance experiments of various existing measures are conducted and their performance is compared in terms of major performance metrics. As a result, the proposed measure reveals better or comparable achievements in all the metrics considered.

KEYWORDS

Collaborative Filtering, Recommender System, Similarity Measure, Entropy

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

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

Using User Rating Patterns for Selecting Neighbors in Collaborative Filtering

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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

Using User Rating Patterns for Selecting Neighbors in Collaborative Filtering

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