International Journal of Science, Engineering and TechnologyAuthors: Dr Chunnulal, Dr Satendra kumar, Dr Raj kumar
Abstract: Modern digital applications have incorporated recommendation systems, which enable platforms to provide users with personalized and relevant content. The aim of these systems is to predict user preferences by identifying patterns in large datasets, which will improve user experience and reduce information overload. E-commerce, movie and music streaming services, digital libraries, social networks, and online news platforms are among the many domains where they have been widely adopted. Prominent examples include Amazon recommending products based on previous purchases, Netflix suggesting movies based on viewing history, and music platforms like Spotify and Deezer generating customized playlists. A recommendation system generally gathers interaction data like ratings, browsing history, and time spent on content, then uses computational techniques to predict what users might want to choose next. The simplicity and strong predictive capabilities of collaborative filtering has made it one of the most widely used methods among various approaches. The assumption of collaborative filtering is that users with similar preferences in the past will have similar interests in the future. The filtering approach can be categorized as user-based and item-based. User-based filtering is used to identify users who have similar rating patterns, while item-based filtering is used to find similarities between items by analyzing how users interact with them. Python and collaborative filtering techniques are used to develop a movie recommendation system in this research. The dataset employed contains key attributes, including user IDs, movie ratings, item identifiers, and time spent on each item. Estimating how likely a user is to enjoy a particular movie is part of the recommendation process by analyzing similar users' preferences. After generating predicted ratings, the system ranks the movies and suggests those with the highest predicted scores. To create a functional recommendation system that can deliver personalized movie suggestions, collaborative filtering can be effectively implemented, as demonstrated in this study. Correlations and similarity measurements are used by the system to analyze user behavior patterns and provide relevant recommendations that enhance user engagement. Today's digital landscape is marked by the importance of recommendation systems, as shown by the results.
DOI: http://doi.org/10.5281/zenodo.20338889
