DMBIN: A Dual Multi-behavior Interest Network for Click-Through Rate Prediction via Contrastive Learning

Click-through rate (CTR) prediction plays a critical role in various online applications, aiming to estimate the user's click probability. User interest modeling from various interactive behaviors(e.g., click, add-to-cart, order) is becoming a mainstream approach to CTR prediction. We argue that the various user behaviors contain two important intrinsic characteristics: 1) The discrepancy in various behaviors reveals different aspects of user's behavior-specific interests. For example, one may click out of need but pay more attention to the rating when purchasing. 2) The consistency of various behaviors contains user's behavior-invariant interest. For example, the user prefers interacted items rather than other items. Therefore, it is necessary to disentangle the discrepancy and consistency signals from the massive behavior information. Unfortunately, previous methods have yet to study this phenomenon well, which limits the recommendation performance.