HS-GCN: Hamming Spatial Graph Convolutional Networks for Recommendation

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.

References in corpus (10)

• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Neural Graph Collaborative Filtering
• Graph Convolutional Neural Networks for Web-Scale Recommender Systems
• KGAT: Knowledge Graph Attention Network for Recommendation
• Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
• Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
• Item Silk Road: Recommending Items from Information Domains to Social Users
• Interpretable and Efficient Heterogeneous Graph Convolutional Network
• User Diverse Preference Modeling by Multimodal Attentive Metric Learning
• Dissecting the Diffusion Process in Linear Graph Convolutional Networks

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