Click-through rate prediction model based on a deep neural network

The click-through rate (CTR) prediction task is to estimate the probability that a user will click an item according to the features of user, item, and contexts. At present, CTR prediction has become a common and indispensable task in the field of e-commerce. Higher accuracy of CTR prediction results conduces to present more accurate and personalized results for recommendation systems and search engines to increase users’ actual CTR of items and bring more economic benefits. More researchers used a deep neural network (DNN) to solve the CTR prediction problem under the background of big data technology in recent years. However, there are a few models that can process time series data and fully consider the context information of users’ history effectively and efficiently. CTR prediction models based on a DNN learn users’ interests from their history; however, most of the existing models regard user interest, ignoring the differences between the long-term and short-term interests. This paper proposes a CTR prediction model named Long- and Short-Term Interest Network (LSTIN) to fully use the context information and order information of user history records. This use will help improve the accuracy and training efficiency of the CTR prediction model. Based on the attention mechanism, the transformer and activation unit structure are used to model long-term and short-term user interests. The latter is processed using the recurrent and convolutional neural networks further. Eventually, a fully-connected neural network is applied for prediction. Different from DeepFM and Deep Interest Network (DIN) in experiments on an Amazon public dataset, LSTIN achieves modeling with context and order information of user history. The AUC of LSTIN is 85.831%, which is 1.154% higher than that of BaseModel and 0.476% higher than that of DIN. Besides, LSTIN achieves distinguishing the long-term and short-term interests of users, which improves the performance and maintains the training efficiency of the CTR prediction model.