Staff View: Cross domain recommender systems: A systematic literature review

Cross domain recommender systems: A systematic literature review

Cross domain recommender systems (CDRS) can assist recommendations in a target domain based on knowledge learned from a source domain. CDRS consists of three building blocks: domain, user-item overlap scenarios, and recommendation tasks. The objective of this research is to identify the most widely...

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Main Authors:	Khan, M. M., Ibrahim, R., Ghani, I.
Format:	Article
Published:	Association for Computing Machinery 2017
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.utm.my/id/eprint/80819/ http://dx.doi.org/10.1145/3073565
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id	my.utm.80819
record_format	eprints
spelling	my.utm.808192019-07-23T09:01:51Z http://eprints.utm.my/id/eprint/80819/ Cross domain recommender systems: A systematic literature review Khan, M. M. Ibrahim, R. Ghani, I. QA75 Electronic computers. Computer science Cross domain recommender systems (CDRS) can assist recommendations in a target domain based on knowledge learned from a source domain. CDRS consists of three building blocks: domain, user-item overlap scenarios, and recommendation tasks. The objective of this research is to identify the most widely used CDRS building-block definitions, identify common features between them, classify current research in the frame of identified definitions, group together research with respect to algorithm types, present existing problems, and recommend future directions for CDRS research. To achieve this objective, we have conducted a systematic literature review of 94 shortlisted studies. We classified the selected studies using the tag-based approach and designed classification grids. Using classification grids, it was found that the category-domain contributed a maximum of 62%, whereas the time domain contributed at least 3%. User-item overlaps were found to have equal contribution. Single target domain recommendation task was found at a maximum of 78%, whereas cross-domain recommendation task had a minor influence at only 10%. MovieLens contributed the most at 22%, whereas Yahoo-music provided 1% between 29 datasets. Factorization-based algorithms contributed a total of 37%, whereas semantics-based algorithms contributed 6% among seven types of identified algorithm groups. Finally, future directions were grouped into five categories. Association for Computing Machinery 2017 Article PeerReviewed Khan, M. M. and Ibrahim, R. and Ghani, I. (2017) Cross domain recommender systems: A systematic literature review. ACM Computing Surveys, 50 (3). ISSN 0360-0300 http://dx.doi.org/10.1145/3073565 DOI:10.1145/3073565
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	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Khan, M. M. Ibrahim, R. Ghani, I. Cross domain recommender systems: A systematic literature review
description	Cross domain recommender systems (CDRS) can assist recommendations in a target domain based on knowledge learned from a source domain. CDRS consists of three building blocks: domain, user-item overlap scenarios, and recommendation tasks. The objective of this research is to identify the most widely used CDRS building-block definitions, identify common features between them, classify current research in the frame of identified definitions, group together research with respect to algorithm types, present existing problems, and recommend future directions for CDRS research. To achieve this objective, we have conducted a systematic literature review of 94 shortlisted studies. We classified the selected studies using the tag-based approach and designed classification grids. Using classification grids, it was found that the category-domain contributed a maximum of 62%, whereas the time domain contributed at least 3%. User-item overlaps were found to have equal contribution. Single target domain recommendation task was found at a maximum of 78%, whereas cross-domain recommendation task had a minor influence at only 10%. MovieLens contributed the most at 22%, whereas Yahoo-music provided 1% between 29 datasets. Factorization-based algorithms contributed a total of 37%, whereas semantics-based algorithms contributed 6% among seven types of identified algorithm groups. Finally, future directions were grouped into five categories.
format	Article
author	Khan, M. M. Ibrahim, R. Ghani, I.
author_facet	Khan, M. M. Ibrahim, R. Ghani, I.
author_sort	Khan, M. M.
title	Cross domain recommender systems: A systematic literature review
title_short	Cross domain recommender systems: A systematic literature review
title_full	Cross domain recommender systems: A systematic literature review
title_fullStr	Cross domain recommender systems: A systematic literature review
title_full_unstemmed	Cross domain recommender systems: A systematic literature review
title_sort	cross domain recommender systems: a systematic literature review
publisher	Association for Computing Machinery
publishDate	2017
url	http://eprints.utm.my/id/eprint/80819/ http://dx.doi.org/10.1145/3073565
_version_	1643658526253383680
score	13.211869

Cross domain recommender systems: A systematic literature review

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