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.

aware multi-behavior collaborative graph under the hier-archically structured graph transformer network. To jointly integrate user- and item-wise collaborative similarities under the multi-behavior modeling paradigm of KHGT: i) the first-stage graph-structured transformer module captures the type-specific user-item interactive

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