Upper confidence bound integrated genetic algorithm‐optimized long short‐term memory network for click‐through rate prediction

Abstract

AbstractData on online advertising is rising rapidly due to the fast development of science and technology. Click‐through rate (CTR) prediction has become a critical task regarding the digital advertising industry and a key element in increasing advertising profits and user experience. Therefore, this article describes the problem of CTR prediction as a function of sequence classification tasks. Then, we proposed a novel optimization strategy to solve the high‐dimensional problem and find a subset of relevant variables to ensure high performance of our model and maximize the number of clicks. Here, we introduced a feature selection and hyper‐parameter optimization approach using genetic algorithms (GA) and the upper confidence bound (UCB) model to optimize micro‐targeting technology, along with the long short‐term memory (LSTM) network‐based CTR prediction model. The efficiency of the proposed UCB‐LSTM‐GA model and two hybrid models, namely LSTM‐GA and LSTM‐PSO, is evaluated by comparing them to each other and to other machine‐learning‐based classification methods, including LSTM using a UCB algorithm (UCB‐LSTM), High‐order Attentive Factorization Machine (HoAFM), genetic algorithm‐artificial neural network (GA‐ANN), and a feature interaction graph neural network model (Fi‐GNN). Our solution achieved as high as 87%, 89%, and 92% for respectively accuracy, precision, and recall, using the popular python tools with real Avazu datasets.