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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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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/
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record_format eprints
spelling 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
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Algorithms
spellingShingle 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.]
description 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.
format Article
author 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/
_version_ 1800100599169024000
score 13.211869

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