Discrete Manifold-Regularized Collaborative Filtering for Large-Scale Recommender Systems

Deming Zhai, Ao Li, Yang Li, Yang Liu, GuoJun Liu, Xianming Liu, Maozu Guo

Abstract: In many online web services, precisely recommending relevant items from massive candidates is a crucial yet computationally expensive task. To confront with the scalability issue, discrete latent factors learning is advocated since it permits exact top-K item recommendation with sub-linear time complexity. However, the performance of existing discrete methods is limited due to they only consider the cross-view user-item relations. In this paper, we propose a new method called Discrete Manifold-Regularized Collaborative Filtering (DMRCF), which jointly exploits cross-view user-item relations and intra-view user-user/item-item affinities in the hamming space. On one hand, inspired by the observation that similar users are more likely to prefer similar items, manifold regularization terms are introduced to enforce similar users/items have similar binary codes in the hamming space. Accordingly, our method is able to learn more about the preference of user and then recommend items based on user’s attributes. On the other hand, for cross-view relations, we cast the reconstruction errors of user-item rating matrix and ranking loss for relative preferences of users into a joint learning framework. Due to latent factors are restricted to be binary values, the optimization is generally a challenging NP-hard problem. To reduce the quantization error, we develop an efficient algorithm to solve the overall discrete optimization problem. Experiments on two real-world datasets demonstrate that DMRCF outperforms the state-of-the-art methods significantly.