Collaborative Learning based Recommendation System for Content Streaming Platform using Non-Negative Matrix Factorization Clustering

Abstract

The Recommender System helps users find relevant content by leveraging their interest models. This paper focuses on content-based recommender systems, which determine recommendations based on previously selected products without needing user evaluations. These systems can now use machine learning to profile both consumers and items. We introduce a collaborative learning method for content streaming platforms using Non-Negative Matrix Factorization Clustering (NNMFC). The research employs a sliding window technique for clustering, implemented with gradient descent and further optimized using Particle Swarm Optimization (PSO). The model's performance was examined through its Root Mean Square Error (RMSE) across three techniques: gradient descent, sliding window gradient descent, and sliding window PSO. The result analysis of the proposed work achieved the lowest RMSE of 0.90, outperforming existing state-of-the-art approaches such as Collaborative Filtering, K-Mean, and UPCSim. The Sliding Window PSO emerged as the most proficient, presenting a notable 9% enhancement over existing methods.