Deep User Multi-interest Network for Click-Through Rate Prediction

Abstract

Click-through rate (CTR) prediction is widely used in recommendation systems. Accurately modeling user interest is the key to improve the performance of CTR prediction task. Existing methods pay attention to model user interest from a single perspective to reflect user preferences, ignoring user different interests in different aspects, thus limiting the expressive ability of user interest. In this paper, we propose a novel Deep User Multi-Interest Network (DUMIN) which designs Self-Interest Extraction Network (SIEN) and User-User Interest Extraction Network (UIEN) to capture user different interests. First, SIEN uses attention mechanism and sequential network to focus on different parts in self-interest. Meanwhile, an auxiliary loss network is used to bring extra supervision for model training. Next, UIEN adopts multi-headed self-attention mechanism to learn a unified interest representation for each user who interacted with the candidate item. Then, attention mechanism is introduced to adaptively aggregate these interest representations to obtain user-user interest, which reflects the collaborative filtering information among users. Extensive experimental results on public real-world datasets show that proposed DUMIN outperforms various state-of-the-art methods.