Movie Recommendation Algorithm Using Social Network Analysis to Alleviate Cold-Start Problem

Khamphaphone Xinchang, Phonexay Vilakone and Doo-Soon Park
Volume: 15, No: 3, Page: 616 ~ 631, Year: 2019
10.3745/JIPS.04.0121
Keywords: Cold Start Problem, Collaborative Filtering (CF), Movie Recommendation System, Social Network Analysis
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Abstract
With the rapid increase of information on the World Wide Web, finding useful information on the internet has become a major problem. The recommendation system helps users make decisions in complex data areas where the amount of data available is large. There are many methods that have been proposed in the recommender system. Collaborative filtering is a popular method widely used in the recommendation system. However, collaborative filtering methods still have some problems, namely cold-start problem. In this paper, we propose a movie recommendation system by using social network analysis and collaborative filtering to solve this problem associated with collaborative filtering methods. We applied personal propensity of users such as age, gender, and occupation to make relationship matrix between users, and the relationship matrix is applied to cluster user by using community detection based on edge betweenness centrality. Then the recommended system will suggest movies which were previously interested by users in the group to new users. We show shown that the proposed method is a very efficient method using mean absolute error.

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Cite this article
IEEE Style
K. Xinchang, P. Vilakone and D. Park, "Movie Recommendation Algorithm Using Social Network Analysis to Alleviate Cold-Start Problem," Journal of Information Processing Systems, vol. 15, no. 3, pp. 616~631, 2019. DOI: 10.3745/JIPS.04.0121.

ACM Style
Khamphaphone Xinchang, Phonexay Vilakone, and Doo-Soon Park. 2019. Movie Recommendation Algorithm Using Social Network Analysis to Alleviate Cold-Start Problem, Journal of Information Processing Systems, 15, 3, (2019), 616~631. DOI: 10.3745/JIPS.04.0121.