Collaborative Group-Aware Hashing for Fast Recommender Systems

TL;DR

This paper introduces a Collaborative Group-Aware Hashing (CGAH) method that enhances fast online recommendation accuracy in sparse data scenarios by incorporating inherent group information into hash code learning.

Contribution

The paper proposes a novel CGAH approach that integrates user and item group affinities into hashing for improved recommendation accuracy in sparse settings.

Findings

CGAH outperforms state-of-the-art discrete recommendation methods.

CGAH-CF achieves higher accuracy in sparse data scenarios.

Extensive experiments validate the effectiveness of the proposed method.

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.