A Non-sequential Approach to Deep User Interest Model for CTR Prediction
Keke Zhao, Xing Zhao, Qi Ping Cao, Linjian Mo · Society for Industrial and Applied Mathematics eBooks · 2022
Click-Through Rate (CTR) prediction plays an important role in many industrial applications, and recently a lot of deep learning methods attempt to improve the performance through feature interaction or user interest models. However, the existing methods fails to explore the potential interest from user behavior sequence effectively. In this paper, we proposed a Non-sequential Interest Interaction Network (NIIN) to tackle this problem. Specifically, we first design the key-vector storage method to construct the sparse representation for any input behavior sequences. Moreover, our NIIN framework introduces a Time Interest Interaction (TII) module to explore interactions among interests. It first includes an efficient fine-grained attention mechanism to score items in behavior sequence for reserving user's diverse interests. After that, our TII module exploits multidimensional partition layer to segment behavior sequence in temporal dimension, and sum pooling operation obtains user's interest in different periods. Furthermore, several fully connect layers model interactions among multiple interests to mine potential interest and give the final score of the target item. Experiments on CTR datasets including Alipay, Taobao and Alimama datasets demonstrate that the NIIN framework outperforms the state-of-the-art methods.