Content-based filtering for recommendation systems using multiattribute networks

Jieun Son, Seoung Bum Kim

Research output: Contribution to journalArticle

25 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)404-412
Number of pages9
JournalExpert Systems with Applications
Volume89
DOIs
Publication statusPublished - 2017 Dec 15

Fingerprint

Recommender systems
Information use
Electric network analysis

Keywords

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

ASJC Scopus subject areas

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

Cite this

Content-based filtering for recommendation systems using multiattribute networks. / Son, Jieun; Kim, Seoung Bum.

In: Expert Systems with Applications, Vol. 89, 15.12.2017, p. 404-412.

Research output: Contribution to journalArticle

@article{7bf9ad3569014abab62ad7e56cde41ed,
title = "Content-based filtering for recommendation systems using multiattribute networks",
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.",
keywords = "Content-based filtering, Movie recommendation, Network analysis, Recommender system",
author = "Jieun Son and Kim, {Seoung Bum}",
year = "2017",
month = "12",
day = "15",
doi = "10.1016/j.eswa.2017.08.008",
language = "English",
volume = "89",
pages = "404--412",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - Content-based filtering for recommendation systems using multiattribute networks

AU - Son, Jieun

AU - Kim, Seoung Bum

PY - 2017/12/15

Y1 - 2017/12/15

N2 - 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.

AB - 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.

KW - Content-based filtering

KW - Movie recommendation

KW - Network analysis

KW - Recommender system

UR - http://www.scopus.com/inward/record.url?scp=85026878727&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85026878727&partnerID=8YFLogxK

U2 - 10.1016/j.eswa.2017.08.008

DO - 10.1016/j.eswa.2017.08.008

M3 - Article

AN - SCOPUS:85026878727

VL - 89

SP - 404

EP - 412

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

ER -