Mean-reversion based hybrid movie recommender system using collaborative and content-based filtering methods

Machine Learning algorithms have a variety of important applications, and among them, Recommender systems are crucial. The internet hosts an extensive volume information, making it challenging for users to navigate find relevant content. therefore emerged as valuable tools bridge this gap. They facilitate the connection between content by offering personalized recommendations. In recent years recommendation service has become hotspot web technology, is widely used in shopping, film television, etc [1]. been proved be response information overload problem [17]. research paper, we describe our approach Movie System Utilizing Mean Reversion via Bollinger Bands formulae. Collaborative filtering popular technique systems. However, poses challenge form cold start issue, where new added system without any ratings, filter unable offer useful recommendations due lack understanding their preferences. Similarly, newly released movies ratings also suffer from same leading reinforcing themselves.To address challenge, incorporated concept Reversion, which fundamental component Natural Mathematics. helps mitigating issue bringing into fold system.Mean reversion statistical that refers tendency series values return its long-term average after experiencing temporary fluctuations. context systems, can estimating rating movie adjusting based on user's preference. This help improve accuracy recommendations, particularly sufficient data.