Precomputed Clustering for Movie Recommendation System in Real Time

A recommendation system delivers customized data (articles, news, images, music, movies, etc.) to its users. As the interest of recommendation systems grows, we started working on the movie recommendation systems. Most research efforts in the fields of movie recommendation system are focusing on dis...

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Bibliographic Details
Main Authors: Bo Li, Yibin Liao, Zheng Qin
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:Journal of Applied Mathematics
Online Access:http://dx.doi.org/10.1155/2014/742341
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spelling doaj-c3a7b3bfce604fa3a5e88af4580cf3f12020-11-24T22:04:07ZengHindawi LimitedJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/742341742341Precomputed Clustering for Movie Recommendation System in Real TimeBo Li0Yibin Liao1Zheng Qin2Department of Computer Science, University of Georgia, Athens, GA 30602, USADepartment of Computer Science, University of Georgia, Athens, GA 30602, USASchool of Software, Tsinghua University, Beijing 100084, ChinaA recommendation system delivers customized data (articles, news, images, music, movies, etc.) to its users. As the interest of recommendation systems grows, we started working on the movie recommendation systems. Most research efforts in the fields of movie recommendation system are focusing on discovering the most relevant features from users, or seeking out users who share same tastes as that of the given user as well as recommending the movies according to the liking of these sought users or seeking out users who share a connection with other people (friends, classmates, colleagues, etc.) and make recommendations based on those related people’s tastes. However, little research has focused on recommending movies based on the movie’s features. In this paper, we present a novel idea that applies machine learning techniques to construct a cluster for the movie by implementing a distance matrix based on the movie features and then make movie recommendation in real time. We implement some different clustering methods and evaluate their performance in a real movie forum website owned by one of our authors. This idea can also be used in other types of recommendation systems such as music, news, and articles.http://dx.doi.org/10.1155/2014/742341
collection DOAJ
language English
format Article
sources DOAJ
author Bo Li
Yibin Liao
Zheng Qin
spellingShingle Bo Li
Yibin Liao
Zheng Qin
Precomputed Clustering for Movie Recommendation System in Real Time
Journal of Applied Mathematics
author_facet Bo Li
Yibin Liao
Zheng Qin
author_sort Bo Li
title Precomputed Clustering for Movie Recommendation System in Real Time
title_short Precomputed Clustering for Movie Recommendation System in Real Time
title_full Precomputed Clustering for Movie Recommendation System in Real Time
title_fullStr Precomputed Clustering for Movie Recommendation System in Real Time
title_full_unstemmed Precomputed Clustering for Movie Recommendation System in Real Time
title_sort precomputed clustering for movie recommendation system in real time
publisher Hindawi Limited
series Journal of Applied Mathematics
issn 1110-757X
1687-0042
publishDate 2014-01-01
description A recommendation system delivers customized data (articles, news, images, music, movies, etc.) to its users. As the interest of recommendation systems grows, we started working on the movie recommendation systems. Most research efforts in the fields of movie recommendation system are focusing on discovering the most relevant features from users, or seeking out users who share same tastes as that of the given user as well as recommending the movies according to the liking of these sought users or seeking out users who share a connection with other people (friends, classmates, colleagues, etc.) and make recommendations based on those related people’s tastes. However, little research has focused on recommending movies based on the movie’s features. In this paper, we present a novel idea that applies machine learning techniques to construct a cluster for the movie by implementing a distance matrix based on the movie features and then make movie recommendation in real time. We implement some different clustering methods and evaluate their performance in a real movie forum website owned by one of our authors. This idea can also be used in other types of recommendation systems such as music, news, and articles.
url http://dx.doi.org/10.1155/2014/742341
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