Representation Learning with Pair-wise Constraints for Collaborative Ranking

Last decades have witnessed a vast amount of interest and research in recommendation systems. Collaborative filtering, which uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users, is one of the most successful approaches to build recommendation systems. Most previous collaborative filtering approaches employ the matrix factorization techniques to learn latent user feature profiles and item feature profiles. Also many subsequent works are proposed to incorporate users' social network information and items' attributions to further improve recommendation performance under the matrix factorization framework. However, the matrix factorization based methods may not make full use of the rating information, leading to unsatisfying performance. Recently deep learning has been approved to be able to find good representations in natural language processing, image classification, and so on. Along this line, we propose a collaborative ranking framework via representation learning with pair-wise constraints (REAP for short), in which autoencoder is used to simultaneously learn the latent factors of both users and items and pair-wise ranked loss defined by (user, item) pairs is considered. Extensive experiments are conducted on five data sets to demonstrate the effectiveness of the proposed framework.