Decision Evaluation Network driven by User Preferences and Dynamic Interests for Click-Through Rate Prediction

Gang Hua, Qing Xie, Yuhan Wang, Lin Li, Mengzi Tang, Yongjian Liu

Abstract: As a key problem in the field of recommender systems, Click-Through Rate (CTR) prediction has garnered significant attention due to its pivotal role in industrial applications. In recent years, numerous CTR prediction models have emerged, mainly focusing on feature interactions and user interest modeling. However, existing approaches are one-sided and tend to ignore the respective effects of users’ relatively stable discrete preferences and continuous dynamic interests. In addition, current models usually overlook the decision-making process behind users’ clicks, making the predicted results difficult to interpret. To address these issues, this paper introduces the Decision Evaluation Network driven by User Preferences and Dynamic Interests (UPDI-DEN), which innovatively reframes the CTR prediction task as a problem of evaluating user click decisions. The proposed model exhibits an advanced capability to distinctly capture the discrete preferences and dynamic interests embedded within users’ history sequences, and explicitly model their respective impacts on the decision-making process governing final click behavior. Experimental results demonstrate the effectiveness and strong competitiveness of the proposed method on three datasets.

External IDs:dblp:conf/ijcnn/HuaXWLTL25