Core Interest Network for Click-Through Rate Prediction

Abstract

In modern online advertising systems, the click-through rate (CTR) is an important index to measure the popularity of an item. It refers to the ratio of users who click on a specific advertisement to the number of total users who view it. Predicting the CTR of an item in advance can improve the accuracy of the advertisement recommendation. And it is commonly calculated based on users’ interests. Thus, extracting users’ interests is of great importance in CTR prediction tasks. In the literature, a lot of studies treat the interaction between users and items as sequential data and apply the recurrent neural network (RNN) model to extract users’ interests. However, these solutions cannot handle the case when the sequence length is relatively long, e.g., over 100. This is because of the vanishing gradient problem of RNN, i.e., the model cannot learn a users’ previous behaviors that are too far away from the current moment. To address this problem, we propose a new Core Interest Network (CIN) model to mitigate the problem of a long sequence in the CTR prediction task with sequential data. In brief, we first extract the core interests of users and then use the refined data as the input of subsequent learning tasks. Extensive evaluations on real dataset show that our CIN model can outperform the state-of-the-art solutions in terms of prediction accuracy.