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Journal of Information Science and Engineering, Vol. 30 No. 2, pp. 387-401 (March 2014)


An Ordinal Regression Model with SVD Hebbian Learning for Collaborative Recommendation


TE-MIN CHANG1, WEN-FENG HSIAO2 AND WEI-LUN CHANG3
1Department of Information Management
National Sun Yat-sen University
Kaohsiung, 804 Taiwan
2Department of Information Management
National Pingtung Institute of Commerce
Pingtung, 900 Taiwan
3Department of Business Administration
Tamkang University
New Taipei City, 251 Taiwan

The Internet is disseminating more and more information as it continues to grow. This large amount of information, however, can cause an information overload problem for users. Recommender systems to help predict user preferences for new information can ease users mental loads. The model-based collaborative filtering (CF) approach and its variants for recommender systems have recently received considerable attention. Nonetheless, two issues should be carefully considered in practical applications. First, the data reliability of the rating matrix can affect the prediction performance. Second, most current models view the measurement scale of output classes as nominal instead of ordinal ratings. This study proposes a model-based CF approach that deals with both issues. Specifically, this approach uses the Hebbian learning rule to facilitate singular value decomposition in reducing noisy and redundant data, and employs support vector ordinal regression to build up the models. The results of the experiments conducted in this study show that the proposed approach outperforms other methods, especially under data of mild data sparsity and large-scale conditions. The feasibility of the proposed approach is justified accordingly.

Keywords: recommender systems, collaborative filtering, model-based CF, data reliability, ordinal scale

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Received February 13, 2012; revised April 21 & June 6, 2012; accepted July 23, 2012.
Communicated by Hsin-Hsi Chen.