Recommendation Algorithm-Driven Product Popularity Prediction: A Data Analytics Perspective

Abstract

This paper explores the optimization of recommendation systems using gradient boosting machine learning models. Traditional recommendation algorithms, such as collaborative filtering, often struggle with sparsity and cold start problems. Gradient boosting offers a robust alternative, capable of capturing complex interactions between users and items while handling both categorical and numerical data effectively. This study examines the theoretical foundations of gradient boosting and discusses optimization techniques, including regularization, hyperparameter tuning, and ensembling, that enhance recommendation system performance. Without relying on specific datasets, this work provides insights into the practical applications of gradient boosting in e-commerce, content streaming, and social media, and outlines future research directions for further refinement of these systems.