Learning Intents behind Interactions with Knowledge Graph for Recommendation

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.

WWW 2021 oral presentation

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• DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation
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• A Survey on Intent-aware Recommender Systems
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• Transition Propagation Graph Neural Networks for Temporal Networks
• M2GNN: Metapath and Multi-interest Aggregated Graph Neural Network for Tag-based Cross-domain Recommendation
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• Towards Explainable Collaborative Filtering with Taste Clusters Learning
• LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN Architecture
• MixRec: Individual and Collective Mixing Empowers Data Augmentation for Recommender Systems
• LogicRec: Recommendation with Users' Logical Requirements
• TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems
• Mitigating Spurious Correlations for Self-supervised Recommendation
• KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?
• Knowledge-Enhanced Recommendation with User-Centric Subgraph Network
• Generalized Relation Learning with Semantic Correlation Awareness for Link Prediction
• Improving Location Recommendation with Urban Knowledge Graph
• LaSER: Language-Specific Event Recommendation
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• Intent Propagation Contrastive Collaborative Filtering
• Topic-Aware Knowledge Graph with Large Language Models for Interoperability in Recommender Systems
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