PRINT: Personalized Relevance Incentive Network for CTR Prediction in Sponsored Search

Click-Through Rate (CTR) prediction plays a critical role in sponsored search. Modeling the semantic relevance between queries and ads is one of the most crucial factors affecting the performance of CTR prediction. However, different users have different sensitivities to semantic relevance due to their personalized relevance preferences. Therefore, semantic relevance may have different incentives on the user's click probability (i.e., stimulative incentive, inhibitive incentive, or irrelevant incentive). Unfortunately, few works have studied the phenomenon, which ignores the complicated incentive effects of semantic relevance and limits the performance of CTR prediction.