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


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AbstractRecommendation, 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.
All Author(s) ListWang D., King I.
Name of Conference17th International Conference on Neural Information Processing, ICONIP 2010
Start Date of Conference22/11/2010
End Date of Conference25/11/2010
Place of ConferenceSydney, NSW
Country/Region of ConferenceAustralia
Detailed descriptionorganized by the Asia-Pacific Neural Network Assembly (APNNA),
Year2010
Month12
Day21
Volume Number6443 LNCS
Issue NumberPART 1
PublisherSpringer Verlag
Place of PublicationGermany
Pages397 - 404
ISBN3642175368
ISSN0302-9743
LanguagesEnglish-United Kingdom
KeywordsGreen's function, item graph, recommender system, semi-supervised learning

Last updated on 2021-19-04 at 00:10