Image Recommendation With Reciprocal Social Influence

Image recommendation plays an important role for exploring user potential interests in large-scale image sharing websites (e.g., Flickr and Instagram). Social relationships have been exploited to learn user preference, and shown their effectiveness. We argue that their performance improvement tends to be limited, as most existing approaches only consider the side of social influence from friends to a user. However, social influence is reciprocal per se as the preference of friends will be also influenced by the user herself. In this paper, we propose a deep neural network for image recommendation (dubbed RSIM) by leveraging reciprocal social influence, and optimize the preferences of users and friends simultaneously. Specifically, we split images into three types: positive image by an active user, social image by her social friends, and negative image by neither of them. We contend that a user prefers positive image to social image, which is in turn better than negative image for relative preference learning. Two neural networks are designed to capture user and image representations by tags and visual features, respectively. The proposed model is evaluated on a real dataset crawled from Flickr. The experimental results show that better performance can be reached than the state-of-the-art social image recommendation models in terms of precision.