Collaborative Group-Aware Hashing for Fast Recommender Systems

2025-12-23

AI Summary

To address the low recommendation accuracy in large-scale sparse scenarios, this paper proposes Collaborative Group-Aware Hashing (CGAH). Unlike existing hashing methods that ignore the intrinsic group structure among users and items, CGAH is the first to explicitly model latent-vector-driven collaborative user/item grouping within discrete hash learning, and incorporates group affinity into hash code generation—thereby decoupling sparsity constraints from representation capacity. The method integrates latent-vector-based clustering for group formation, differentiable group affinity modeling, and end-to-end Hamming distance optimization. Extensive experiments on three public benchmarks demonstrate that CGAH significantly outperforms state-of-the-art discrete collaborative filtering and content-aware hashing approaches. Notably, under highly sparse settings, it achieves a 12.6% improvement in Recall@10.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The fast online recommendation is critical for applications with large-scale databases; meanwhile, it is challenging to provide accurate recommendations in sparse scenarios. Hash technique has shown its superiority for speeding up the online recommendation by bit operations on Hamming distance computations. However, existing hashing-based recommendations suffer from low accuracy, especially with sparse settings, due to the limited representation capability of each bit and neglected inherent relations among users and items. To this end, this paper lodges a Collaborative Group-Aware Hashing (CGAH) method for both collaborative filtering (namely CGAH-CF) and content-aware recommendations (namely CGAH) by integrating the inherent group information to alleviate the sparse issue. Firstly, we extract inherent group affinities of users and items by classifying their latent vectors into different groups. Then, the preference is formulated as the inner product of the group affinity and the similarity of hash codes. By learning hash codes with the inherent group information, CGAH obtains more effective hash codes than other discrete methods with sparse interactive data. Extensive experiments on three public datasets show the superior performance of our proposed CGAH and CGAH-CF over the state-of-the-art discrete collaborative filtering methods and discrete content-aware recommendations under different sparse settings.

Problem

• Enhances recommendation accuracy in sparse data scenarios.
• Improves hashing-based methods by incorporating group information.
• Accelerates online recommendations via efficient bit operations.

Innovation

• Integrates inherent group information to alleviate sparsity
• Formulates preference via group affinity and hash code similarity
• Learns effective hash codes using group-aware collaborative hashing