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Published in:
2010 | OriginalPaper | Chapter
An Enhanced Semi-supervised Recommendation Model Based on Green’s Function
Authors : Dingyan Wang, Irwin King
Published in: Neural Information Processing. Theory and Algorithms
Publisher: Springer Berlin Heidelberg
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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 multi-class semi-supervised learning framework. Finally, experimental results on real world data demonstrate the effectiveness of our model.