Adaptive Random Forest-based Algorithm for Fast Advertisement Recommendation

Abstract

In the increasingly competitive landscape of online advertising, the need for efficient and effective advertisement recommendation algorithms has become more pressing. Existing research has primarily focused on improving recommendation accuracy but often faces challenges such as scalability and computational complexity. This paper addresses these challenges by proposing an Adaptive Random Forest-based Algorithm for Fast Advertisement Recommendation. By combining the adaptability of Random Forest with the efficiency of a fast recommendation process, our approach aims to provide timely and accurate recommendations to users while maintaining scalability. The innovative aspect of our work lies in the adaptive nature of the algorithm, which dynamically adjusts to changing user preferences and feedback. Through experimental evaluation, we demonstrate the effectiveness and efficiency of our proposed algorithm in the context of advertisement recommendation.