Context Aware Personalized Movie Recommendation System

Abstract

ABSTRACT In the era of digital, the volume of content has been overwhelming greater than before, bringing users the dilemma between the plenitude and quality of choices, making the personalized recommendation systems more essential than ever. The CAPMRS represents a notable improvement in the field of recommendation systems which deal with observed limitations of traditional approach and overcome them by incorporating contextual information to for personalization and enhance user satisfaction. Through this study, we propose a multidimensional (CAPMRS) approach that blends user preferences, content dimensions, and contextual factors (such as time of day, mood, and social setting) to create highly personalized movie recommendations. Using collaborative filtering, content-based filtering, and advanced machine learning algorithms, CAPMRS analyzes both the user interactions, movie metadata and contextual information effectively to generate and find recommendations. The system is distinctive for its ability to distinguish the changing nature of a user’s choice which can be highly dependent on situational contexts. CAPMRS attempts to give out suggestions, which are not just tailored to the users’ past preferences but also congruent with their current situation, by integrating contextual features; this is to improve user experience. CAPMRS comes with infrastructure and related challenges which include data privacy, computational complexity, and accurate capture of contextual information, and these are areas that require more research. Context-Aware Personalized Movie Recommendation System is a futuristic approach to content recommendation showing the real advantages of interweaving contextual information into personalized algorithms. This research thus provides invaluable insights for the design of more advanced recommendation systems tailored to user preferences, which constitutes a foundation for further improvements in digital material selection and discovery.