Learning Intents behind Interactions with Knowledge Graph for Recommendation

TL;DR

This paper introduces KGIN, a novel GNN-based model that captures user intents at a fine-grained level using auxiliary knowledge and relational paths, improving recommendation accuracy and interpretability.

Contribution

The paper proposes a new model, KGIN, which models user intents as attentive combinations of KG relations and recursively integrates relational paths for enhanced recommendation and interpretability.

Findings

KGIN outperforms state-of-the-art methods on benchmark datasets.

KGIN provides interpretable explanations for recommendations.

Relational path integration improves long-range connectivity modeling.

Abstract

Knowledge graph (KG) plays an increasingly important role in recommender systems. A recent technical trend is to develop end-to-end models founded on graph neural networks (GNNs). However, existing GNN-based models are coarse-grained in relational modeling, failing to (1) identify user-item relation at a fine-grained level of intents, and (2) exploit relation dependencies to preserve the semantics of long-range connectivity. In this study, we explore intents behind a user-item interaction by using auxiliary item knowledge, and propose a new model, Knowledge Graph-based Intent Network (KGIN). Technically, we model each intent as an attentive combination of KG relations, encouraging the independence of different intents for better model capability and interpretability. Furthermore, we devise a new information aggregation scheme for GNN, which recursively integrates the relation sequences of long-range connectivity (i.e., relational paths). This scheme allows us to distill useful information about user intents and encode them into the representations of users and items. Experimental results on three benchmark datasets show that, KGIN achieves significant improvements over the state-of-the-art methods like KGAT, KGNN-LS, and CKAN. Further analyses show that KGIN offers interpretable explanations for predictions by identifying influential intents and relational paths. The implementations are available at https://github.com/huangtinglin/Knowledge_Graph_based_Intent_Network.