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PAKDD
2015
ACM

Coupling Multiple Views of Relations for Recommendation

4 years 12 months ago
Coupling Multiple Views of Relations for Recommendation
Learning user/item relation is a key issue in recommender system, and existing methods mostly measure the user/item relation from one particular aspect, e.g., historical ratings, etc. However, the relations between users/items could be influenced by multifaceted factors, so any single type of measure could get only a partial view of them. Thus it is more advisable to integrate measures from different aspects to estimate the underlying user/item relation. Furthermore, the estimation of underlying user/item relation should be optimal for current task. To this end, we propose a novel model to couple multiple relations measured on different aspects, and determine the optimal user/item relations via learning the optimal way of integrating these relation measures. Specifically, matrix factorization model is extended in this paper by considering the relations between latent factors of different users/items. Experiments are conducted and our method shows good performance and outperforms o...
Bin Fu, Guandong Xu, Longbing Cao, Zhihai Wang, Zh
Added 16 Apr 2016
Updated 16 Apr 2016
Type Journal
Year 2015
Where PAKDD
Authors Bin Fu, Guandong Xu, Longbing Cao, Zhihai Wang, Zhiang Wu
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