Advertising Click-Through Rate Prediction Model Based on an Attention Mechanism and a Neural Network

Abstract

With the rapid development of the Internet and the rapid expansion of the advertising market, display advertising has become the most popular means of publicity. Accurate advertising recommendation is the guarantee of Internet platform revenue, and accurate advertising click-through rate prediction is the premise of accurate recommendation. According to the requirements of the update rate and real-time of the advertising platform, the coupling relationship of the advertising business module can be divided into two categories: offline business module and online business module. The offline advertising management module is mainly based on the construction of the mathematical model, which is used to mine the complex relationship between users and commodity characteristics; the online advertising management module is mainly based on the real-time feedback of users and changes the recommendation strategy in real time by collecting feedback information. For the offline advertising management module, an advertising click-through rate prediction model based on attention mechanism and neural network is proposed, which is called CAN. This method provides richer feature interaction information for a neural network layer. For the online advertising management module, a real-time recommendation algorithm based on Gaussian process is proposed. This method can solve the universality of specific function assumptions in various environments. Through the coupling of the advertising click-through rate prediction model constructed by the offline advertising management module and the real-time recommendation algorithm of the online advertising management module, the accurate delivery of advertising can be realized.