A CTR prediction model based on session interest

Abstract

Click-through rate prediction has become a hot research direction in the field of advertising. It is important to build an effective CTR prediction model. However, most existing models ignore the factor that the sequence is composed of sessions, and the user behaviors are highly correlated in each session and are not relevant across sessions. In this paper, we focus on user multiple session interest and propose a hierarchical model based on session interest (SIHM) for CTR prediction. First, we divide the user sequential behavior into session layer. Then, we employ a self-attention network obtain an accurate expression of interest for each session. Since different session interest may be related to each other or follow a sequential pattern, next, we utilize bidirectional long short-term memory network (BLSTM) to capture the interaction of different session interests. Finally, the attention mechanism based LSTM (A-LSTM) is used to aggregate their target ad to find the influences of different session interests. Experimental results show that the model performs better than other models.

Advertisement design in dynamic interactive scenarios using DeepFM and long short-term memory (LSTM)

“…Because deep neural networks have high autonomous representational capabilities, they can be used to forecast click-through rate (CTR), which is a very promising application of deep learning technology. Deep neural network-based models have been widely proposed and have produced accurate prediction results ( Wang et al, 2022 ; Guo et al, 2017 ). These DL models now have two improved parts.…”