Ensemble Learning Approach for Efficient Recommendation Systems Using Semi-Supervised Learning

In recommender systems, collaborative filtering (CF) is a crucial technique, but it often struggles with data sparsity, which affects recommendation accuracy. To address this challenge, we have proposed a Co-Training Ensemble Learning (CTEL) technique that integrates item-based Collaborative Filtering (CF), user-based CF, and Singular Value Decomposition (SVD) via a structured stacking methodology to improve recommendation performance. The co-training procedure, which creates pseudo-labels for unlabeled data based on a confidence threshold, is used to iteratively improve the user-based and item-based CF models after they have been originally trained. These models produce predictions for validation and test sets, in conjunction with the independently trained SVD model. These forecasts yield meta-features, including additional statistical variables such as variance and the product of predictions. The Linear Regression model is trained as the meta-learner to optimise the predictions of the base models using K-Fold cross-validation. Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) are used to assess the final model’s performance on a test set. The outcomes confirm the effectiveness of the co-training and stacking strategy, demonstrating notable increases in prediction accuracy. By leveraging the advantages of collaborative filtering and matrix-based approaches, the proposed model provides a comprehensive foundation for developing advanced recommendation systems.