A movie recommendations: a collaborative filtering approach implemented in Python / Nor Syazana Abdul Kodit ... [et al.]
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-Bas...
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UiTM Cawangan Perlis
2024
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my.uitm.ir.943612024-05-03T09:34:49Z https://ir.uitm.edu.my/id/eprint/94361/ A movie recommendations: a collaborative filtering approach implemented in Python / Nor Syazana Abdul Kodit ... [et al.] jcrinn Abdul Kodit, Nor Syazana Tajul Rosli Razak, Razak Ismail, Mohammad Hafiz Hashim, Shakirah Tengku Petra, Tengku Zatul Hidayah Mansor, Nur Farraliza Algorithms 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. UiTM Cawangan Perlis 2024-03 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/94361/1/94361.pdf A movie recommendations: a collaborative filtering approach implemented in Python / Nor Syazana Abdul Kodit ... [et al.]. (2024) Journal of Computing Research and Innovation (JCRINN) <https://ir.uitm.edu.my/view/publication/Journal_of_Computing_Research_and_Innovation_=28JCRINN=29/>, 9 (1): 20. pp. 257-268. ISSN 2600-8793 |
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Algorithms Abdul Kodit, Nor Syazana Tajul Rosli Razak, Razak Ismail, Mohammad Hafiz Hashim, Shakirah Tengku Petra, Tengku Zatul Hidayah Mansor, Nur Farraliza A movie recommendations: a collaborative filtering approach implemented in Python / Nor Syazana Abdul Kodit ... [et al.] |
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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. |
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Abdul Kodit, Nor Syazana Tajul Rosli Razak, Razak Ismail, Mohammad Hafiz Hashim, Shakirah Tengku Petra, Tengku Zatul Hidayah Mansor, Nur Farraliza |
author_facet |
Abdul Kodit, Nor Syazana Tajul Rosli Razak, Razak Ismail, Mohammad Hafiz Hashim, Shakirah Tengku Petra, Tengku Zatul Hidayah Mansor, Nur Farraliza |
author_sort |
Abdul Kodit, Nor Syazana |
title |
A movie recommendations: a collaborative filtering approach implemented in Python / Nor Syazana Abdul Kodit ... [et al.] |
title_short |
A movie recommendations: a collaborative filtering approach implemented in Python / Nor Syazana Abdul Kodit ... [et al.] |
title_full |
A movie recommendations: a collaborative filtering approach implemented in Python / Nor Syazana Abdul Kodit ... [et al.] |
title_fullStr |
A movie recommendations: a collaborative filtering approach implemented in Python / Nor Syazana Abdul Kodit ... [et al.] |
title_full_unstemmed |
A movie recommendations: a collaborative filtering approach implemented in Python / Nor Syazana Abdul Kodit ... [et al.] |
title_sort |
movie recommendations: a collaborative filtering approach implemented in python / nor syazana abdul kodit ... [et al.] |
publisher |
UiTM Cawangan Perlis |
publishDate |
2024 |
url |
https://ir.uitm.edu.my/id/eprint/94361/1/94361.pdf https://ir.uitm.edu.my/id/eprint/94361/ |
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1800100599169024000 |
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13.18916 |