Cluster-Enhanced Dual Discrete Collaborative Filtering for Efficient Recommendation

作者：Fan Wang, Chaochao Chen, Weiming Liu, Lianyong Qi, Xuyun Zhang, Yanchao Tan, Mengying Zhu, Xiaolin Zheng · 发表于：IEEE Transactions on Knowledge and Data Engineering · 年份：2025 · DOI：10.1109/tkde.2025.3624659 · 被引用次数：3 · 研究领域：Recommender Systems and Techniques、Advanced Technologies in Various Fields、Privacy-Preserving Technologies in Data

Hash-based collaborative filtering (Hash-CF) approaches recently employ efficient Hamming distance of learned binary representations to accelerate recommendations. Benefiting from its probabilistic nature, Variational Autoencoder (VAE) enables robust Hash-CF with stronger generalization ability. However, VAE-based Hash-CF still faces two challenging problems: 1) Traditional VAE urges the latent variables of different users (or items) to fit a unified and monotonous prior distribution, and lacks considerations for distinctive characteristics of users (or items). The obtained representations of users and items with slight individual differentiation may further weaken the performance of Hash-CF for subsequent personalized recommendations. 2) Hash-CF under the VAE framework requires discrete optimization on latent Bernoulli distributions, which are discrete and NP-hard to optimize. In this paper, we propose a Dual Discrete Collaborative Filtering (DDCF) approach, including a cluster-enhanced representation generation module and a CNF-enabled discrete optimization module. The former module mainly develops cluster-aware latent space to generate discriminative representations for users or items with significantly different characteristics. The latter module employs Continuous Normalizing Flow (CNF) to achieve discrete optimization on latent Bernoulli distributions steadily and effectively. Extensive experiments conducted on multiple real-world datasets demonstrate the superiority of...