Every week, we select a recently published Open Access article to feature. This week’s article is from Applied Stochastic Models in Business and Industry and studies the problem of click-through rate predictions for the digital advertising industry.
The article’s abstract is given below, with the full article available to read here.
, . Upper confidence bound integrated genetic algorithm-optimized long short-term memory network for click-through rate prediction. Appl Stochastic Models Bus Ind. 2022; 38( 3): 475– 496. doi:10.1002/asmb.2671
Data 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.