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A survey on data mining techniques in recommender systems

Recommender systems have been regarded as gaining a more significant role with the emergence of the first research article on collaborative filtering (CF) in the mid-1990s. CF predicts the interests of an active user based on the opinions of users with similar interests. To extract information on th...

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Main Authors: Najafabadi, M. K., Mohamed, A. H., Mahrin, M. N.
Format: Article
Published: Springer Verlag 2019
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/77173/
http://dx.doi.org/10.1007/s00500-017-2918-7
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spelling my.utm.771732020-11-02T04:01:54Z http://eprints.utm.my/id/eprint/77173/ A survey on data mining techniques in recommender systems Najafabadi, M. K. Mohamed, A. H. Mahrin, M. N. T Technology (General) Recommender systems have been regarded as gaining a more significant role with the emergence of the first research article on collaborative filtering (CF) in the mid-1990s. CF predicts the interests of an active user based on the opinions of users with similar interests. To extract information on the preference of users for a set of items and evaluate the performance of the recommender system’s techniques and algorithms, a critical analysis can be conducted. This study therefore employs a critical analysis on 131 articles in CF area from 36 journals published between the years 2010 and 2016. This analysis seems to be the exclusive survey which supports and motivates the community of researchers and practitioners. It is done by using the applications of users’ activities and intelligence computing and data mining techniques on CF recommendation systems. In addition, it provides a classification of the literature on academic database according to the benchmark recommendation databases, two users’ feedbacks (explicit and implicit feedbacks) which reflect their activities and categories of intelligence computing and data mining techniques. Eventually, this study provides a road map to guide future direction on recommender systems research and facilitates the accumulated and derived knowledge on the application of intelligence computing and data mining techniques in CF recommendation systems. Springer Verlag 2019-01-30 Article PeerReviewed Najafabadi, M. K. and Mohamed, A. H. and Mahrin, M. N. (2019) A survey on data mining techniques in recommender systems. Soft Computing, 23 (2). pp. 627-654. ISSN 1432-7643 http://dx.doi.org/10.1007/s00500-017-2918-7 DOI:10.1007/s00500-017-2918-7
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic T Technology (General)
spellingShingle T Technology (General)
Najafabadi, M. K.
Mohamed, A. H.
Mahrin, M. N.
A survey on data mining techniques in recommender systems
description Recommender systems have been regarded as gaining a more significant role with the emergence of the first research article on collaborative filtering (CF) in the mid-1990s. CF predicts the interests of an active user based on the opinions of users with similar interests. To extract information on the preference of users for a set of items and evaluate the performance of the recommender system’s techniques and algorithms, a critical analysis can be conducted. This study therefore employs a critical analysis on 131 articles in CF area from 36 journals published between the years 2010 and 2016. This analysis seems to be the exclusive survey which supports and motivates the community of researchers and practitioners. It is done by using the applications of users’ activities and intelligence computing and data mining techniques on CF recommendation systems. In addition, it provides a classification of the literature on academic database according to the benchmark recommendation databases, two users’ feedbacks (explicit and implicit feedbacks) which reflect their activities and categories of intelligence computing and data mining techniques. Eventually, this study provides a road map to guide future direction on recommender systems research and facilitates the accumulated and derived knowledge on the application of intelligence computing and data mining techniques in CF recommendation systems.
format Article
author Najafabadi, M. K.
Mohamed, A. H.
Mahrin, M. N.
author_facet Najafabadi, M. K.
Mohamed, A. H.
Mahrin, M. N.
author_sort Najafabadi, M. K.
title A survey on data mining techniques in recommender systems
title_short A survey on data mining techniques in recommender systems
title_full A survey on data mining techniques in recommender systems
title_fullStr A survey on data mining techniques in recommender systems
title_full_unstemmed A survey on data mining techniques in recommender systems
title_sort survey on data mining techniques in recommender systems
publisher Springer Verlag
publishDate 2019
url http://eprints.utm.my/id/eprint/77173/
http://dx.doi.org/10.1007/s00500-017-2918-7
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score 13.211869

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