An enhanced semi-supervised recommendation model based on Green's function
Refereed conference paper presented and published in conference proceedings

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摘要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. © 2010 Springer-Verlag.
著者Wang D., King I.
會議名稱17th International Conference on Neural Information Processing, ICONIP 2010
會議開始日22.11.2010
會議完結日25.11.2010
會議地點Sydney, NSW
會議國家/地區澳大利亞
詳細描述organized by the Asia-Pacific Neural Network Assembly (APNNA),
出版年份2010
月份12
日期21
卷號6443 LNCS
期次PART 1
出版社Springer Verlag
出版地Germany
頁次397 - 404
國際標準書號3642175368
國際標準期刊號0302-9743
語言英式英語
關鍵詞Green's function, item graph, recommender system, semi-supervised learning

上次更新時間 2021-10-05 於 01:03