Machine Learning for Recommender Systems Under Implicit Feedback and Class Imbalance

Abstract

Recommender systems (RS) in domains with implicit feedback and significant class imbalance, such as health insurance, face unique challenges in accurately predicting user preferences. This study proposes a machine learning framework leveraging tree-based ensemble methods to address these limitations. We conducted a comprehensive comparative analysis of algorithms, including Decision Trees, Random Forest, Gradient Boosting Machines, CatBoost, Extra Trees, HistGradient Boosting, and XGBoost to identify the most effective approach for handling data skew and complex feature interactions. The model was trained on a real-world dataset from an international insurance broker, containing demographic profiles and purchase histories. After extensive preprocessing and class rebalancing, the models were optimized and evaluated on a separate test set. Among these, XGBoost verified superior performance, achieving remarkable results with a precision of 97.23% and an accuracy of 97.51%. The model presented robust generalization capabilities and convergence stability, with no signs of overfitting. Concretely, these performances translate into an increased ability for insurers to reliably identify customer needs from limited behavioral data, thus improving the relevance of personalized offers. These findings highlight the efficacy of XGBoost in treatment datasets with unbalanced implicit feedback and its capacity as an effective solution for complex recommendation problems. This work contributes a practical and scalable framework for improving personalized recommendations in data-constrained environments.