HS-GCN: Hamming Spatial Graph Convolutional Networks for Recommendation

TL;DR

HS-GCN introduces a graph convolutional approach to model both first- and high-order similarities in Hamming space, significantly improving large-scale recommendation accuracy by capturing complex user-item relations.

Contribution

The paper presents HS-GCN, a novel framework that explicitly models high-order similarities in Hamming space for recommendation, outperforming existing hashing methods.

Findings

Outperforms state-of-the-art hashing models on benchmark datasets.

Achieves recommendation performance comparable to real-valued models.

Effectively captures high-order user-item similarities in Hamming space.

Abstract

An efficient solution to the large-scale recommender system is to represent users and items as binary hash codes in the Hamming space. Towards this end, existing methods tend to code users by modeling their Hamming similarities with the items they historically interact with, which are termed as the first-order similarities in this work. Despite their efficiency, these methods suffer from the suboptimal representative capacity, since they forgo the correlation established by connecting multiple first-order similarities, i.e., the relation among the indirect instances, which could be defined as the high-order similarity. To tackle this drawback, we propose to model both the first- and the high-order similarities in the Hamming space through the user-item bipartite graph. Therefore, we develop a novel learning to hash framework, namely Hamming Spatial Graph Convolutional Networks (HS-GCN), which explicitly models the Hamming similarity and embeds it into the codes of users and items. Extensive experiments on three public benchmark datasets demonstrate that our proposed model significantly outperforms several state-of-the-art hashing models, and obtains performance comparable with the real-valued recommendation models.