Transcription of Knowledge-Enhanced Hierarchical Graph Transformer …
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Knowledge-Enhanced Hierarchical Graph Transformer …
Knowledge-Enhanced Hierarchical Graph Transformer Networkfor Multi-Behavior RecommendationLianghao Xia1, Chao Huang2 , Yong Xu1,3,4, Peng Dai2, Xiyue Zhang1 Hongsheng Yang2, Jian Pei5, Liefeng Bo2 South China University of Technology1, China, JD Finance America Corporation2, USAC ommunication and Computer Network Laboratory of Guangdong3, ChinaPeng Cheng Laboratory, Shenzhen, China, Simon Fraser University5, user and item embedding learning is crucial formodern recommender systems. However, most existing rec-ommendation techniques have thus far focused on model-ing users preferences over singular type of user-item inter-actions. Many practical recommendation scenarios involvemulti-typed user interactive behaviors ( , page view, add-to-favorite and purchase), which presents unique challengesthat cannot be handled by current recommendation particular: i) complex inter-dependencies across differenttypes of user behaviors; ii) the incorporation of knowledge-aware item rela
ior hierarchical dependencies and discriminates the type-specific contribution, in forecasting the target behaviors. We apply the proposed KHGT method to three real-world datasets of movie, venue and product recommendations. Experiments show that our model achieves significant gains over 15 state-of-the-art baselines from various lines.
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