In-home entertainment, selecting the perfect movie is a pervasive challenge, amplified by many streaming platforms like Netflix and Amazon.This study introduces a groundbreaking Movie Recommendation System with Collaborative Filtering (MRS-CF), meticulously implemented in Python.Employing Item-Based Collaborative Filtering with Cosine Similarity, the system assesses inter-movie relationships based on user-submitted titles, explicitly focusing here on genre distinctions.
The core contribution of MRS-CF lies in its ability to expedite the movie selection process, swiftly presenting users with a curated list of ten recommended movies strategically organised by descending similarity.Augmented with individual similarity scores, this system is crafted to optimise the user’s movie-watching experience.Thirty participants were evaluated through the Perceived Ease of Use (PEOU).
The PEOU results underscore the profound contribution of MRS-CF, revealing elevated user satisfaction across all dimensions.This research illuminates soderhamn ottoman cover the potent impact of the MRS-CF, emphasising its role as a transformative tool for refining and enhancing personalised movie recommendations.