Content-based filtering for recommendation systems using multiattribute networks
- Authors
- Son, Jieun; Kim, Seoung Bum
- Issue Date
- 15-12월-2017
- Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
- Keywords
- Content-based filtering; Recommender system; Movie recommendation; Network analysis
- Citation
- EXPERT SYSTEMS WITH APPLICATIONS, v.89, pp.404 - 412
- Indexed
- SCIE
SCOPUS
- Journal Title
- EXPERT SYSTEMS WITH APPLICATIONS
- Volume
- 89
- Start Page
- 404
- End Page
- 412
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/81175
- DOI
- 10.1016/j.eswa.2017.08.008
- ISSN
- 0957-4174
- Abstract
- Content-based filtering (CBF), one of the most successful recommendation techniques, is based on correlations between contents. CBF uses item information, represented as attributes, to calculate the similarities between items. In this study, we propose a novel CBF method that uses a multiattribute network to effectively reflect several attributes when calculating correlations to recommend items to users. In the network analysis, we measure the similarities between directly and indirectly linked items. Moreover, our proposed method employs centrality and clustering techniques to consider the mutual relationships among items, as well as determine the structural patterns of these interactions. This mechanism ensures that a variety of items are recommended to the user, which improves the performance. We compared the proposed approach with existing approaches using MovieLens data, and found that our approach outperformed existing methods in terms of accuracy and robustness. Our proposed method can address the sparsity problem and over-specialization problem that frequently affect recommender systems. Furthermore, the proposed method depends only on ratings data obtained from a user's own past information, and so it is not affected by the cold start problem. (C) 2017 Elsevier Ltd. All rights reserved.
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Collections - College of Engineering > School of Industrial and Management Engineering > 1. Journal Articles
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