Graph-powered recommendation engine in movie recommender system

Usually, people will search on the Internet for movie that they want to watch. However, it is tedious to find and choose movie that matched with their preferences due to a lot of information on the Internet. Therefore, most of the movie portals are adopting recommendation engine to filter and displa...

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Main Authors: Abdul Mutalib, Ratna Dewi, Hassan, Saadah, Chew, Yew Chong, Admodisastro, Novia Indriaty, Baharom, Salmi
Format: Article
Language:English
Published: Advanced Scientific Research 2020
Online Access:http://psasir.upm.edu.my/id/eprint/89130/1/GRAPH.pdf
http://psasir.upm.edu.my/id/eprint/89130/
https://www.bibliomed.org/?mno=117306
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spelling my.upm.eprints.891302021-09-03T23:51:12Z http://psasir.upm.edu.my/id/eprint/89130/ Graph-powered recommendation engine in movie recommender system Abdul Mutalib, Ratna Dewi Hassan, Saadah Chew, Yew Chong Admodisastro, Novia Indriaty Baharom, Salmi Usually, people will search on the Internet for movie that they want to watch. However, it is tedious to find and choose movie that matched with their preferences due to a lot of information on the Internet. Therefore, most of the movie portals are adopting recommendation engine to filter and display user’s personalized content. In this paper, MyRecMovie is presented to recommend movies by using graph-powered recommendation engine. MyRecMovie adopts content based (CB) and collaborative filtering (CF) approaches with then further enhance the recommendations with graph-powered recommendation engine to provide movie recommendations to the user. Advanced Scientific Research 2020 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/89130/1/GRAPH.pdf Abdul Mutalib, Ratna Dewi and Hassan, Saadah and Chew, Yew Chong and Admodisastro, Novia Indriaty and Baharom, Salmi (2020) Graph-powered recommendation engine in movie recommender system. Journal of Critical Reviews, 7 (8). 1269 - 1274. ISSN 2394-5125 https://www.bibliomed.org/?mno=117306 10.31838/jcr.07.08.261
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Usually, people will search on the Internet for movie that they want to watch. However, it is tedious to find and choose movie that matched with their preferences due to a lot of information on the Internet. Therefore, most of the movie portals are adopting recommendation engine to filter and display user’s personalized content. In this paper, MyRecMovie is presented to recommend movies by using graph-powered recommendation engine. MyRecMovie adopts content based (CB) and collaborative filtering (CF) approaches with then further enhance the recommendations with graph-powered recommendation engine to provide movie recommendations to the user.
format Article
author Abdul Mutalib, Ratna Dewi
Hassan, Saadah
Chew, Yew Chong
Admodisastro, Novia Indriaty
Baharom, Salmi
spellingShingle Abdul Mutalib, Ratna Dewi
Hassan, Saadah
Chew, Yew Chong
Admodisastro, Novia Indriaty
Baharom, Salmi
Graph-powered recommendation engine in movie recommender system
author_facet Abdul Mutalib, Ratna Dewi
Hassan, Saadah
Chew, Yew Chong
Admodisastro, Novia Indriaty
Baharom, Salmi
author_sort Abdul Mutalib, Ratna Dewi
title Graph-powered recommendation engine in movie recommender system
title_short Graph-powered recommendation engine in movie recommender system
title_full Graph-powered recommendation engine in movie recommender system
title_fullStr Graph-powered recommendation engine in movie recommender system
title_full_unstemmed Graph-powered recommendation engine in movie recommender system
title_sort graph-powered recommendation engine in movie recommender system
publisher Advanced Scientific Research
publishDate 2020
url http://psasir.upm.edu.my/id/eprint/89130/1/GRAPH.pdf
http://psasir.upm.edu.my/id/eprint/89130/
https://www.bibliomed.org/?mno=117306
_version_ 1710677165354254336
score 13.160551