A click-through rate prediction model incorporating user review text and behavior sequence

Abstract

There is a high correlation between user behavior and user features in recommender systems. User review texts reflect user preferences and item feature information. However, the current research on CTR prediction models based on user behaviors fails to fully mine user features. As a result, the prediction accuracy of the model is not high. To solve this problem, we propose a click-through rate prediction model that fuses user comment text and behavior sequence. The model uses a text convolutional neural network to extract the features of user review text to obtain the feature vector of user comment text, and uses an attention mechanism to capture the user's interest points from the user's behavior sequence to obtain the user's interest feature vector. A multi-layer perceptron is then used to fuse the user's comment text feature vector, interest feature vector and item feature vector for click-through rate prediction. The experimental results show that the proposed model has better prediction performance than current click-through rate prediction models.