Yuri Ariyanto, Triyanna Widiyanigtyas, Ilham Ari Elbaith Zaeni
This research presents a new Demographic-Enhanced Cosine-KNN method for collaborative filtering in recommender systems. Our method demonstrates superior performance compared to state-of-the-art techniques across various datasets, indicating substantial enhancements in recommendation accuracy. Assessments of the MovieLens 100K and 1M datasets demonstrate significant improvements in RMSE and MAE metrics relative to traditional KNN-Basic and advanced ExtKNNCF algorithms. The proposed method demonstrates improvements of up to 17.1% in RMSE and 14.4% in MAE compared to KNN-Basic, while consistently exceeding ExtKNNCF by margins ranging from 2.0% to 10.1%. Our method demonstrates significant improvement compared to the standard Cosine-KNN approach, achieving enhancements of 1.9% in RMSE and 2.4% in MAE for the 100K dataset, and 0.7% in RMSE and 1.9% in MAE for the 1M dataset. The consistent gains observed across various sample sizes indicate the stability and scalability of the strategy employed. The results highlight the efficacy of our demographic-enhanced strategy in overcoming the limitations of current collaborative filtering methods, providing a scalable and robust solution for enhancing recommendation accuracy across various application contexts. © (2024), (Intelligent Network and Systems Society). All Rights Reserved.
Department of Electrical Engineering and Informatics, Universitas Negeri Malang, Malang, Indonesia; Department of Information Technology, Politeknik Negeri Malang, Malang, Indonesia
