A Novel Embedding Model for Relation Prediction in Recommendation Systems

Yu ZHAO  Sheng GAO  Patrick GALLINARI  Jun GUO  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E100-D   No.6   pp.1242-1250
Publication Date: 2017/06/01
Publicized: 2017/03/14
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016EDP7421
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
Keyword: 
recommendation system,  latent factor model,  relation prediction,  cross-domain recommendation,  

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Summary: 
It inevitably comes out information overload problem with the increasing available data on e-commence websites. Most existing approaches have been proposed to recommend the users personal significant and interesting items on e-commence websites, by estimating unknown rating which the user may rate the unrated item, i.e., rating prediction. However, the existing approaches are unable to perform user prediction and item prediction, since they just treat the ratings as real numbers and learn nothing about the ratings' embeddings in the training process. In this paper, motivated by relation prediction in multi-relational graph, we propose a novel embedding model, namely RPEM, to solve the problem including the tasks of rating prediction, user prediction and item prediction simultaneously for recommendation systems, by learning the latent semantic representation of the users, items and ratings. In addition, we apply the proposed model to cross-domain recommendation, which is able to realize recommendation generation in multiple domains. Empirical comparison on several real datasets validates the effectiveness of the proposed model. The data is available at https://github.com/yuzhaour/da.