Convolutional Attention Model for Restaurant Recommendation with Multi-View Visual Features.

Current recommendation systems usually use image as a type of side information to enhance recommendation systems. However, there are few models to analyze and apply the effectiveness of different categories of images. In order to effectively combine multi-category image information in the recommendation system, we propose a novel deep network model with different categories of images as side information for recommendation, in which we use convolutional attention to analyze the importance of images within different image categories. The convolutional layers of the attention module are used to evaluate the user's visual preferences for images in different categories. The prediction model uses the cosine similarity of user factors and restaurant factors incorporating visual information. Visual features of the restaurants are extracted from multi-category images by a pre-trained neural network. We apply our model to two real-world restaurant recommendation data sets. Experimental results show that the performance of our model is better than models without or with only one category visual information.