Multi-view Visual Bayesian Personalized Ranking from Implicit Feedback

Haihua Luo, Xiaoyan Zhang, Bowei Chen, Guibing Guo

Abstract: In this paper, we propose a new factorization model that combines multi-view visual feature information with the implicit feedback data for prediction and ranking. The visual information is integrated into a collaborative filtering framework. The visual features of images are extracted by using a deep neural network. In order to conduct personalized recommendation better, the multi-view visual features are fused through user related weights. The user related weights reflect the personalized visual preference for items. They are different and independent between users. Experimental results show that our model with multi-view visual information achieves the better performance than models without or with only single-view visual information.