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

Abstract

In recommender systems, collaborative filtering (CF) is a crucial technique, but it often struggles with the data sparsity issue, which impact the recommendation accuracy. To address this challenge we have proposed a Co-training Ensemble Learning (CTEL) technique that integrates item-based Collaborative Filtering, user-based collaborative filtering (CF), and Singular Value Decomposition (SVD) through a structured stacking methodology to improve the 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, which include extra statistical variables like variance and product of predictions. The Linear Regression model is trained as the meta-learner to find the prediction of the base models in the best possible way 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 by showing notable increases in prediction accuracy when compared with existing techniques. Using the advantages of collaborative filtering approaches and matrix-based approach, the proposed model offers a comprehensive foundation for creating advanced recommendation systems.