[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.9708/jksci.2019.24.09.077

Using User Rating Patterns for Selecting Neighbors in Collaborative Filtering

Lee, Soojung (Dept. of Computer Education, Gyeongin National University of Education)

Publication Information

Journal of the Korea Society of Computer and Information / v.24, no.9, 2019 , pp. 77-82 More about this Journal

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;

Citations & Related Records

Reference

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