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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2014 Vol.4(2): 127-132 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.399

Trust-Aware Recommender System Incorporating Review Contents

Hideyuki Mase, Katsutoshi Kanamori, and Hayato Ohwada
Abstract—Personalized recommendation systems can help people find things that interest them and are widely used in developing the Internet or e-commerce. Collaborative filtering (CF) seems to be the most popular technique in recommender systems. However, CF is weak in the process of finding similar users. To resolve these problems, trust-aware recommender systems (TaRSs) have been developed in recent years. In this study, we propose a new approach that incorporates the content of reviews in a TaRS. In addition, we use a new dataset that is collected from the Yahoo!Movie website, whereas traditional research has used Epinions or Movielens. Finally, we evaluate the experiment results using precision and coverage.

Index Terms—Collaborative filtering, content of reviews, trust network, Yahoo!Movie dataset.

Hideyuki Mase, Katsutoshi Kanamori, and Hayato Ohwada are with the Department of Industrial Administration, Graduate School of Science and Technology, Tokyo University of Science, Yamazaki 2641, Noda-City, Chiba, Japan (e-mail: h-mase@ohwada-lab.net, katsu@rs.tus.ac.jp, ohwada@ia.noda.tus.ac.jp).

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Cite: Hideyuki Mase, Katsutoshi Kanamori, and Hayato Ohwada, "Trust-Aware Recommender System Incorporating Review Contents," International Journal of Machine Learning and Computing vol.4, no. 2, pp. 127-132, 2014.

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