CTR Prediction Models Considering the Dynamics of User Interest

Abstract

Click-through rate (CTR) prediction is one of the key areas in industrial bidding advertising. Recently, to improve prediction performance, researchers have proposed the interest-based deep models that learn the user's latent interest from historical click behaviors. However, the interest-based deep models lack the ability to trace the dynamics of interest from both the positive and negative samples, which leading to prediction errors. In particular, user's interest in candidate advertisements could be positive (i.e. interested) or negative (i.e. uninterested), and changing over time. To solve this problem, we propose a deep-based dynamic interest perception network (DIPN) that can trace both positive interest and negative interest. The proposed DIPN model introduces three new parts to the interest-based deep model: a gated recurrent unit (GRU) learns the implied interest vector from historical click sequences; an attention mechanism improves the expressiveness of interest vector with the weight vector; an interest degree feature scales the weight vector by the d-Softmax function to improve the expressiveness of interest vector. We evaluate the effectiveness of the DIPN model by conducting extensive comparative experiments using real datasets. The experimental results demonstrate that compared with state-of-the-art models, DIPN achieves the highest prediction performance.