An enhanced semi-supervised recommendation model based on green's function

Authors:
Dingyan Wang;Irwin King
Affiliations:
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong;Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Year:
2010

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Abstract

Recommendation, in the filed of machine learning, is known as a technique of identifying user preferences to new items with ratings from recommender systems. Recently, one novel recommendation model using Green's function treats recommendation as the process of label propagation. Although this model outperforms many standard recommendation methods, it suffers from information loss during graph construction because of data sparsity. In this paper, aiming at solving this problem and improving prediction accuracy, we propose an enhanced semi-supervised Green's function recommendation model. The main contributions are two-fold: 1) To reduce information loss, we propose a novel graph construction method with global and local consistent similarity; 2) We enhance the recommendation algorithm with the multiclass semi-supervised learning framework. Finally, experimental results on real world data demonstrate the effectiveness of our model.