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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 relations into the multi-behavior recommen-dation framework; iii) dynamic characteristics of multi-typed user-item interactions.

Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation Lianghao Xia 1, Chao Huang 2, Yong Xu;3 4, Peng Dai , Xiyue Zhang1 Hongsheng Yang 2, Jian Pei5, Liefeng Bo South China University of Technology1, China, JD Finance America Corporation2, USA Communication and Computer Network Laboratory of …

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