NLP BASED TEXT SUMMARIZATION USING BART MODEL

Abstract

User interest and response prediction are critical tasks in online advertising, Advertisers may more effectively target their advertising, increase their click-through rates (CTRs), and boost their conversion rates by accurately predicting user interests and behaviors. In recent years, deep learning has developed into a potent tool for predicting consumer interest and response. Deep learning models can accurately predict user interests and responses because they are able to understand complicated patterns from huge quantities of user behavior data. In this study, we suggest a deep learning-based system for online advertising response and interest prediction. Two deep-learning models make up our framework: Thismodel forecasts the likelihood that a user will click on an advertisement. Model for predicting user interest: This model forecasts the likelihood that a user would click on a particular kind of advertising campaign. The models are developed using data from a sizable datasetof user behavior, such as user clicks, ad views, anddemographic data. Compared to conventional approaches, our system has several advantages for predicting user interest and response. First, unlike existing methods, our system can identify Key Words: Deep learning, LST model, user interest prediction, response prediction.