Movie recommendation system using AI
Gambhir Singh, Anupam Kumar Saini, Vishal Pratap Singh, Muzammil Abedin, Hammad Alam · 2025
Today&s;s proliferation of digital content platforms and the increase in videos available to audiences require consensus process to enhance user experience and improve search content. This research paper explores the latest advances in movie recommendation systems, focusing on the application of machine learning algorithms, collaborative filtering and deep learning models. The study begins with an in-depth review of traditional agreements highlighting their strengths and limitations. Then we discuss the evolution of self- transformation and the proposal of context-aware algorithms. Particular attention is placed on connecting interests, demographics, and learning opportunities in order to match recommendations based on personal preferences and interests. Additionally, this study looks into the application of deep learning to video recommendation. Explore the ways in which neural networks can enhance your ability to detect complex patterns in user behavior and boost your revenue. This work also explores the integration of deep learning, content-based filtering, and clustering to improve the results of various methodologies. A detailed examination of the film literature as well as the standards recommended by this study were done in order to present an exhaustive assessment. A number of consensus models are assessed according to metrics like accuracy, variety, and seasonality. Furthermore, the research addresses ethical worries regarding consensus algorithms, including those related to confidentiality, transparency, and fairness bias.