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Mathematical Problems in Engineering
Volume 2013, Article ID 957184, 10 pages
Research Article

A Novel Nonadditive Collaborative-Filtering Approach Using Multicriteria Ratings

Department of Business Administration, Chung Yuan Christian University, Chung-Li 32023, Taiwan

Received 13 June 2013; Accepted 23 September 2013

Academic Editor: Jung-Fa Tsai

Copyright © 2013 Yi-Chung Hu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Although single-criterion recommender systems have been successfully used in several applications, multicriteria rating systems which allow users to specify ratings for various content attributes of individual items are gaining importance in recommendation context. An overall rating of an unrated item is often obtained by the weighted average method (WAM) when criterion weights are available. However, the assumption of additivity for the WAM is not always reasonable. For this reason, this paper presents a new collaborative-filtering approach using multicriteria ratings, in which a nonadditive technique in Multicriteria decision making (MCDM), namely, the Choquet integral, is used to aggregate multicriteria ratings for unrated items. Subsequently, the system can recommend items with higher overall ratings for each user. The degrees of importance of the respective criteria are determined by a genetic algorithm. In contrast to the additive weighted average aggregation, the Choquet integral does not ignore the interaction among criteria. The applicability of the proposed approach to the recommendation of the initiators on a group-buying website is examined. Experimental results demonstrate that the generalization ability of the proposed approach performs well compared with other similarity-based collaborative-filtering approaches using multicriteria ratings.