Learning Intents behind Interactions with Knowledge Graph for Recommendation
arXiv:2102.07057 · doi:10.1145/3442381.3450133
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
References in corpus (12)
- Semi-Supervised Classification with Graph Convolutional Networks
- BPR: Bayesian Personalized Ranking from Implicit Feedback
- KGAT: Knowledge Graph Attention Network for Recommendation
- Simplifying Graph Convolutional Networks
- Knowledge Graph Convolutional Networks for Recommender Systems
- Brownian distance covariance
- RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
- Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences
- Disentangled Graph Collaborative Filtering
- Reinforcement Knowledge Graph Reasoning for Explainable Recommendation
- DKN: Deep Knowledge-Aware Network for News Recommendation
- Learning Disentangled Representations for Recommendation