Abstract
Knowledge graphs (KGs) are increasingly used to solve the data sparsity and cold start problems of collaborative filtering. Recently, graph neural networks (GNNs) have been applied to build KG-based recommender systems and achieved competitive performance. However, existing GNN-based methods are either limited in their ability to capture fine-grained semantics in a KG, or insufficient in effectively modeling user-item interactions. To address these issues, we propose a novel framework with collaborative and attentive graph convolutional networks for personalized knowledge-aware recommendation. Particularly, we model the user-item graph and the KG separately and simultaneously with an efficient graph convolutional network and a personalized knowledge graph attention network, where the former aims to extract informative collaborative signals, while the latter is designed to capture fine-grained semantics. Collectively, they are able to learn meaningful node representations for predicting user-item interactions. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed method compared with state-of-the-arts.
Original language | English |
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Article number | 108628 |
Pages (from-to) | 1-12 |
Journal | Pattern Recognition |
Volume | 128 |
DOIs | |
Publication status | Published - Aug 2022 |
Keywords
- Attention mechanism
- Graph convolutional network
- Knowledge graph
- Recommender system
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence