A Hierarchical User Behavior Modeling Framework for Cross-Domain Click-Through Rate Prediction
Hai Li, Xin Dong, Lei Cheng, Linjian Mo · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · 2022
Click-through rate (CTR) prediction is a long-standing problem in advertising systems. Existing single-domain CTR prediction methods suffer from the data sparsity problem since few users can click advertisements on many items. Recently, cross-domain CTR prediction leverages the relatively richer information from a source domain to improve the performance on a target domain with sparser information, but it cannot explicitly capture users' diverse interests in different domains. In this paper, we propose a novel hierarchical user behavior modeling framework for cross-domain CTR prediction, named HBMNet. HBMNet contains two main components: an element-wise behavior transfer(EWBT) layer and a user representation layer. EWBT layer transfers the information collected from one domain by element-level masks to dynamically highlight the informative elements in another domain. The user representation layer performs behavior-level attention between these behavior representations and the ranking item representation. Extensive experimental results on two cross-domain datasets show that the proposed HBMNet outperforms SOTA models.