Unify Local and Global Information for Top-$N$ Recommendation

TL;DR

This paper introduces a duet framework that combines local user-item interaction data with global knowledge graph information to improve top-N recommendation accuracy, addressing limitations of existing KG-based methods.

Contribution

The paper proposes a novel duet representation learning framework that fuses local and global information for enhanced recommendation performance, with separate sub-models and a semantic fusion network.

Findings

KADM outperforms state-of-the-art methods on real-world datasets.

The duet architecture significantly improves recommendation accuracy.

Ablation studies confirm the effectiveness of combining local and global models.

Abstract

Knowledge graph (KG), integrating complex information and containing rich semantics, is widely considered as side information to enhance the recommendation systems. However, most of the existing KG-based methods concentrate on encoding the structural information in the graph, without utilizing the collaborative signals in user-item interaction data, which are important for understanding user preferences. Therefore, the representations learned by these models are insufficient for representing semantic information of users and items in the recommendation environment. The combination of both kinds of data provides a good chance to solve this problem. To tackle this research gap, we propose a novel duet representation learning framework named \sysname to fuse local information (user-item interaction data) and global information (external knowledge graph) for the top-$N$ recommendation, which is composed of two separate sub-models. One learns the local representations by discovering the inner correlations in local information with a knowledge-aware co-attention mechanism, and another learns the global representations by encoding the knowledge associations in global information with a relation-aware attention network. The two sub-models are jointly trained as part of the semantic fusion network to compute the user preferences, which discriminates the contribution of the two sub-models under the special context. We conduct experiments on two real-world datasets, and the evaluations show that KADM significantly outperforms state-of-art methods. Further ablation studies confirm that the duet architecture performs significantly better than either sub-model on the recommendation tasks.