Neural Binary Representation Learning for Large-Scale Collaborative Filtering

Abstract

Integrating hashing into collaborative filtering (CF) has been a promising solution to address the efficiency problem of large-scale recommender systems, i.e., hashing users and items into binary codes and then making recommendations in Hamming space. However, most of the existing hashing methods for CF solely focus on modeling the user-item similarity (a.k.a. preference) but omit the user-user and item-item similarities, which cannot well preserve the original geometry in the vector space. Another tough issue is how to effectively tackle the encoding loss from the continuous vector space to the discrete Hamming space. In this paper, we propose a neural binary representation (NBR) learning approach by combining hashing with a neural network for the large-scale CF tasks. Our NBR problem is formulated as a Hamming similarity loss plus two anchor smoothing terms, which jointly preserve the intrinsic user-item, user-user, and item-item similarities. To compensate the encoding loss introduced by hashing, the anchors for users and items are pre-learned using both user-item interactions and the side-information of users and items through an AutoEncoder model. In particular, to solve the NBR problem, we develop a computationally efficient algorithm, which learns the binary codes in a fast bit-by-bit way and achieves less quantization deviation than the conventional CF hashing schemes. The extensive experiments on three benchmarks validate the superiority of our NBR approach in comparison to the state-of-the-art CF hashing methods.