A Regularity-Based Preprocessing Method for Collaborative Recommender Systems

Raciel Yera Toledo, Yailé Caballero Mota and Milton García Borroto
Volume: 9, No: 3, Page: 435 ~ 460, Year: 2013
10.3745/JIPS.2013.9.3.435
Keywords: Collaborative Recommender Systems, Inconsistencies, Rating Regularities
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Abstract
Recommender systems are popular applications that help users to identify items that they could be interested in. A recent research area on recommender systems focuses on detecting several kinds of inconsistencies associated with the user preferences. However, the majority of previous works in this direction just process anomalies that are intentionally introduced by users. In contrast, this paper is centered on finding the way to remove non-malicious anomalies, specifically in collaborative filtering systems. A review of the state-of-the-art in this field shows that no previous work has been carried out for recommendation systems and general data mining scenarios, to exactly perform this preprocessing task. More specifically, in this paper we propose a method that is based on the extraction of knowledge from the dataset in the form of rating regularities (similar to frequent patterns), and their use in order to remove anomalous preferences provided by users. Experiments show that the application of the procedure as a preprocessing step improves the performance of a data-mining task associated with the recommendation and also effectively detects the anomalous preferences.

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Cite this article
IEEE Style
R. Y. Toledo and Y. C. M. M. G. Borroto, "A Regularity-Based Preprocessing Method for Collaborative Recommender Systems," Journal of Information Processing Systems, vol. 9, no. 3, pp. 435~460, 2013. DOI: 10.3745/JIPS.2013.9.3.435.

ACM Style
Raciel Yera Toledo, Yailé Caballero Mota and Milton García Borroto. 2013. A Regularity-Based Preprocessing Method for Collaborative Recommender Systems, Journal of Information Processing Systems, 9, 3, (2013), 435~460. DOI: 10.3745/JIPS.2013.9.3.435.