Content-based filtering for recommendation systems using multiattribute networks

Jieun Son, Seoung Bum Kim

Research output: Contribution to journalArticlepeer-review

107 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)404-412
Number of pages9
JournalExpert Systems With Applications
Publication statusPublished - 2017 Dec 15


  • Content-based filtering
  • Movie recommendation
  • Network analysis
  • Recommender system

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence


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