Enhancing Recommendation System using Ontology-based Similarity and Incremental SVD Prediction

With the explosion of data in recent years, recommender systems have become increasingly important for personalized services and enhancing user engagement in various industries, including e-commerce and entertainment. Collaborative filtering (CF) is a widely used approach for generating recommendations, but it has limitations in addressing issues such as sparsity, scalability, and prediction errors. To address these challenges, this study proposes a novel hybrid CF method for movie recommendations that combines an incremental singular value decomposition approach with an item-based ontological semantic filtering approach in both online and offline phases. The ontology-based technique improves the accuracy of predictions and recommendations. The proposed method is evaluated on a real-world movie recommendation dataset using several performance metrics, including precision, F1 scores, and MAE. The results demonstrate that the proposed method outperforms existing methods in terms of accuracy while also addressing sparsity and scalability issues in recommender systems. Additionally, our approach has the advantage of reduced running time, making it a promising solution for practical applications. The proposed method offers a promising solution to the challenges faced by traditional CF methods in recommender systems. By combining incremental SVD and ontological semantic filtering, the proposed method not only improves the accuracy of predictions and recommendations but also addresses issues related to scalability and sparsity. Overall, the proposed method has the potential to contribute to the development of more accurate and efficient recommendation systems in various industries, including e-commerce and entertainment.