Clustering-based Factorization Machines for Advertisement Click prediction

Abstract

Advertisements establish the reputation of an organization and promote its business growth. The advancements in technology have replaced traditional promotions with personalized advertising techniques. Advertisement click prediction utilizes machine learning and deep learning techniques to predict the likelihood of a user clicking on an advertisement. These predictions require intensive computational power to handle large sparse datasets and train complex models that effectively capture feature interactions. In this paper, an efficient method is presented to identify the representative points of the large dataset and capture higher-order interactions of the click features by utilizing K-Means clustering and FNFM (Field Aware Neural Factorization Machine) classifiers, respectively. Our proposed model is trained on 2 lakh representative data points drawn from 1 million samples and is tested with various test sample sizes of 1,2,3,4 million. To show the efficiency of the proposed model, we have evaluated the area under the curve (AUC) results and compared them with the Base models from the literature. Hence, by addressing the above-mentioned factors it is evident that the proposed clustering-based FM model is yielding better results.