MOVIE RECOMMENDATION SYSTEM

The objective of this research is to create a web-based movie recommender system that effectively tackles the challenges of data sparsity and cold start. The system effectively combines explicit data, such as movie ratings and implicit data, including watch history, search queries, and interactions, in order to gain a comprehensive understanding of user preferences. Data cleaning and normalisation are essential preprocessing steps. Sophisticated recommendation algorithms are utilised to improve recommendations, incorporating collaborative filtering, sparsity reduction, and cold start mitigation techniques. The evaluation findings indicate better recommendation, underscoring the system's efficacy in addressing sparsity and cold start obstacles