Temporal user interest modeling for online advertising using Bi-LSTM network improved by an updated version of Parrot Optimizer

In the era of digitization, online digital advertising is one of the best techniques for modern marketing. This makes advertisers rely heavily on accurate user interest and behavior modelling to deliver precise advertisement impressions and increase click-through rates. The classic approach to model user interests has often required the use of predefined feature sets which are typically stagnant and not representative of temporal changes and trends in user behavior. While recent advances in deep learning offer potential solutions to these obstacles, many existing approaches fail to address the sequential nature of user interactions. In this paper, we propose an optimized Bi-Directional Long Short-Term Memory (Bi-LSTM) based user interest modeling approach together with an Updated version of Parrot Optimizer (UPO) to enhance performance. It treats the user activity as a temporal sequence which well fits the changing nature of user interest and preferences over time. The proposed approach is evaluated on two important tasks: predicting the probability that a user will click on an ad and predicting the probability that a user will click on a particular type of ad campaign. Simulation results demonstrate that the proposed method provides superior results than the static set-based approaches and achieves significant improvements on both user ad responses predictions and user ad clicks at the campaign level. The research also enhances the efficiency of user interest modeling with significant implications for online advertising, recommendation systems, and personalized marketing.