Research on data preprocessing method for artificial intelligence algorithm based on user online behavior

Abstract

This paper delves into the significance and efficacy of data preprocessing techniques specifically tailored for user online behavior data in enhancing the performance of artificial intelligence algorithms. Through a comprehensive literature review, we identify gaps and opportunities in current methodologies, setting the stage for the development of a novel data preprocessing approach. This proposed method is meticulously designed to handle the complexities and nuances of user online behavior data. We conduct a series of case studies and rigorous empirical analyses to evaluate the effectiveness of our approach. The results clearly demonstrate that our method substantially improves data quality by effectively reducing noise and eliminating irrelevant information, which, in turn, enhances the overall performance of the AI algorithms. The paper concludes with a discussion on the limitations of the current study and provides insightful directions for future research in the field. This includes potential refinements to the preprocessing technique and its application to other types of behavioral data in different AI domains.