A movie recommendations: a collaborative filtering approach implemented in Python / Nor Syazana Abdul Kodit ... [et al.]

• Main Authors: Abdul Kodit, Nor Syazana, Tajul Rosli Razak, Razak, Ismail, Mohammad Hafiz, Hashim, Shakirah, Tengku Petra, Tengku Zatul Hidayah, Mansor, Nur Farraliza
• Format: Article
• Language: English
• Published: UiTM Cawangan Perlis 2024
• Subjects: Algorithms
• Online Access: https://ir.uitm.edu.my/id/eprint/94361/1/94361.pdf; https://ir.uitm.edu.my/id/eprint/94361/

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 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 the potent impact of the MRS-CF, emphasising its role as a transformative tool for refining and enhancing personalised movie recommendations.