Advancements in machine learning for recommender systems: A focus on NNMFC and particle swarm optimization techniques

Through the use of interest models, the Recommender System assists users in discovering content that is relevant to them. In order to make product suggestions based on past purchases, content-based recommender systems do not require user ratings. These systems are the subject of this study. Now these systems can profile products and customers using machine learning. Together with Non-Negative Matrix Factorization Clustering (NNMFC), we present a new approach to collaborative learning for online video sites. The research utilizes a sliding window clustering approach that relies on Particle Swarm Optimization (PSO) and gradient descent. We utilized three approaches to assess the model's performance: sliding window PSO (SWPSO), sliding window gradient descent and gradient descent. The Root Mean Square Error (RMSE) was calculated for each. Outperforming current state-of-the-art methods like UPCSim, K-Mean, and Collaborative Filtering, the suggested work's result analysis attained the lowest RMSE of 1.02. With a significant improvement of 10.2% over previous techniques, the Sliding Window PSO was shown to be the most effective.