An incremental collaborative filtering algorithm for recommender systems

Publication Type:
Conference Proceeding
Citation:
World Scientific Proc. Series on Computer Engineering and Information Science 7; Uncertainty Modeling in Knowledge Engineering and Decision Making - Proceedings of the 10th International FLINS Conf., 2012, 7 pp. 327 - 332
Issue Date:
2012-12-01
Metrics:
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Recommender systems are effective approaches to implement personalised e-services. In recent years, they have gained widespread applications in e-commerce. Current recommender systems still need, however, further improvements with respect to the accuracy of prediction and to solve the scalability problem. To this end, an incremental collaborative filtering (InCF) algorithm based on the Mahalanobis distance is presented for recommender systems. Furthermore, the Mahalanobis radial basis function with ellipsoidal shape is employed to determine the decision boundaries of clusters. Experimental results show that the algorithm proposed can lead to improved prediction accuracy and that it turns out to be scalable in recommender applications.
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