Learning Intents behind Interactions with Knowledge Graph for Recommendation
Xiang Wang, Tinglin Huang, Dingxian Wang, Yancheng Yuan, Zhenguang Liu, Xiangnan He, Tat‐Seng Chua
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.
Topics & Concepts
Computer scienceInterpretabilityExploitKnowledge graphGraphRelation (database)Recommender systemSemantics (computer science)ENCODETheoretical computer scienceInformation retrievalData miningMachine learningChemistryComputer securityProgramming languageBiochemistryGeneAdvanced Graph Neural NetworksRecommender Systems and TechniquesTopic Modeling