HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation

TL;DR

HCFRec is a novel hash-based collaborative filtering method that uses normalized flow and structural consensus to improve recommendation accuracy and efficiency by effectively learning binary representations.

Contribution

HCFRec introduces normalized flow for optimal hash code learning and a cluster consistency mechanism to preserve semantic structure in representations.

Findings

Outperforms state-of-the-art methods in effectiveness.

Achieves higher efficiency in large-scale datasets.

Demonstrates robustness across six real-world datasets.

Abstract

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.