VRKG4Rec: Virtual Relational Knowledge Graphs for Recommendation

Abstract

Incorporating knowledge graph as side information has become a new trend in recommendation systems. Recent studies regard items as entities of a knowledge graph and leverage graph neural networks to assist item encoding, yet by considering each relation type individually. However, relation types are often too many and sometimes one relation type involves too few entities. We argue that it is not efficient nor effective to use every relation type for item encoding. In this paper, we propose a VRKG4Rec model (Virtual Relational Knowledge Graphs for Recommendation), which explicitly distinguish the influence of different relations for item representation learning. We first construct virtual relational graphs (VRKGs) by an unsupervised learning scheme. We also design a local weighted smoothing (LWS) mechanism for encoding nodes, which iteratively updates a node embedding only depending on the embedding of its own and its neighbors, but involve no additional training parameters. We also employ the LWS mechanism on a user-item bipartite graph for user representation learning, which utilizes encodings of items with relational knowledge to help training representations of users. Experiment results on two public datasets validate that our VRKG4Rec model outperforms the state-of-the-art methods. The implementations are available at https://github.com/lulu0913/VRKG4Rec.

References in corpus (7)

• KGAT: Knowledge Graph Attention Network for Recommendation
• Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences
• Learning Intents behind Interactions with Knowledge Graph for Recommendation
• Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System
• Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning
• Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph
• Dismantling Complex Networks by a Neural Model Trained from Tiny Networks

Cited by in corpus (1)

• Towards Robust Recommendation via Decision Boundary-aware Graph Contrastive Learning