Rule-Guided Graph Neural Networks for Recommender Systems
- URL: http://arxiv.org/abs/2009.04104v1
- Date: Wed, 9 Sep 2020 05:00:02 GMT
- Title: Rule-Guided Graph Neural Networks for Recommender Systems
- Authors: Xinze Lyu and Guangyao Li and Jiacheng Huang and Wei Hu
- Abstract summary: We propose RGRec, which combines rule learning and graph neural networks (GNNs) for recommendation.
We show the effectiveness of RGRec on three real-world datasets.
- Score: 15.973065623038424
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: To alleviate the cold start problem caused by collaborative filtering in
recommender systems, knowledge graphs (KGs) are increasingly employed by many
methods as auxiliary resources. However, existing work incorporated with KGs
cannot capture the explicit long-range semantics between users and items
meanwhile consider various connectivity between items. In this paper, we
propose RGRec, which combines rule learning and graph neural networks (GNNs)
for recommendation. RGRec first maps items to corresponding entities in KGs and
adds users as new entities. Then, it automatically learns rules to model the
explicit long-range semantics, and captures the connectivity between entities
by aggregation to better encode various information. We show the effectiveness
of RGRec on three real-world datasets. Particularly, the combination of rule
learning and GNNs achieves substantial improvement compared to methods only
using either of them.
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