  The ever-increasing data scale of user-item interactions makes it challenging
for an effective and efficient recommender system. Recently, hash-based
collaborative filtering (Hash-CF) approaches employ efficient Hamming distance
of learned binary representations of users and items to accelerate
recommendations. However, Hash-CF often faces two challenging problems, i.e.,
optimization on discrete representations and preserving semantic information in
learned representations. To address the above two challenges, we propose
HCFRec, a novel Hash-CF approach for effective and efficient recommendations.
Specifically, HCFRec not only innovatively introduces normalized flow to learn
the optimal hash code by efficiently fit a proposed approximate mixture
multivariate normal distribution, a continuous but approximately discrete
distribution, but also deploys a cluster consistency preserving mechanism to
preserve the semantic structure in representations for more accurate
recommendations. Extensive experiments conducted on six real-world datasets
demonstrate the superiority of our HCFRec compared to the state-of-art methods
in terms of effectiveness and efficiency.
