An Ensemble Learning Hybrid Recommendation System Using Content-Based, Collaborative Filtering, Supervised Learning and Boosting Algorithms

Abstract

The evolution of recommendation systems has revolutionized user experiences by providing personalized recommendations. Although conventional systems such as collaborative and content-based filtering are reliable, they still suffer from inherent limitations. We introduce a hybrid recommendation system that combines content-based filtering using TF-IDF and cosine similarity with collaborative filtering and SVD to address these challenges. We bolster our model through supervised machine learning algorithms like decision trees (DT), random forests (RF), and support vector regression (SVR). To amplify predictive prowess, boosting algorithms including CatBoost and XGBoost are harnessed. Our experiments are performed on the benchmark dataset MovieLens 1M, which highlights the superiority of our hybrid method over more traditional alternatives with SVR being the best-performing algorithm consistently. Our hybrid model achieved an MSLE score of 2.3 and an RMSLE score of 1.5, making SVR consistently the best-performing algorithm in the recommendation system. This combination demonstrates the potential of collaborative-content hybrids supported by cutting-edge machine-learning techniques to reshape the field of recommendation systems.