HCF-MFGB: Hybrid Collaborative Filtering Based on Matrix Factorization and Gradient Boosting

Recommendation systems are an integral and indispensable part of every digital platform, as they can suggest content or items to users based on their respective needs. Collaborative filtering is a technique often used in various studies, which produces recommendations by analyzing similarities between users and items based on their behavior. Although often used, traditional collaborative filtering techniques still face the main challenge of sparsity. Sparsity problems occur when the data in the system is sparse, meaning that only a portion of users provide feedback on some items, resulting in inaccurate recommendations generated by the system. To overcome this problem, we developed a Hybrid Collaborative Filtering model based on Matrix Factorization and Gradient Boosting (HCF-MFGB), a new hybrid approach. Our proposed model integrates SVD++, the XGBoost ensemble learning algorithm, and utilizes user demographic data and meta items. We utilize information, both explicitly and implicitly, to learn user preference patterns using SVD++. The XGBoost algorithm is used to create hundreds of decision trees incrementally, thereby improving model accuracy. Meanwhile, user demographic and meta-item data are clustered using the K-Means Clustering algorithm to capture similarities in user and item characteristics. This combination is designed to improve rating prediction accuracy by reducing reliance on minimal explicit rating data, while addressing sparsity issues in movie recommendation systems. The results of experiments on the MovieLens 100K, MovieLens 1M, and CiaoDVD datasets show significant improvements, outperforming various other baseline models in terms of RMSE and MAE. On the MovieLens 100K dataset, the HCF-MFGB model obtained an RMSE value of 0.853 and an MAE value of 0.674. On the MovieLens 1M dataset, the HCF-MFGB model obtained an RMSE value of 0.763 and an MAE value of 0.61. On the CiaoDCD dataset, the HCF-MFGB model achieved an RMSE value of 0.718 and an MAE value of 0.495. These results confirm a significant improvement in movie recommendation accuracy with the proposed approach.