Enhancing Movie Recommendations: A Demographic-Integrated Cosine-KNN Collaborative Filtering Approach

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.