The Power of Implicit Social Relation in Rating Prediction of Social Recommender Systems

The explosive growth of social networks in recent times has presented a powerful source of information to be utilized as an extra source for assisting in the social recommendation problems. The social recommendation methods that are based on probabilistic matrix factorization improved the recommenda...

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Main Authors:	Reafee, Waleed, Salim, Naomie, Khan, Atif
Format:	Online
Language:	English
Published:	Public Library of Science 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4859527/

id	pubmed-4859527
recordtype	oai_dc
spelling	pubmed-48595272016-05-13 The Power of Implicit Social Relation in Rating Prediction of Social Recommender Systems Reafee, Waleed Salim, Naomie Khan, Atif Research Article The explosive growth of social networks in recent times has presented a powerful source of information to be utilized as an extra source for assisting in the social recommendation problems. The social recommendation methods that are based on probabilistic matrix factorization improved the recommendation accuracy and partly solved the cold-start and data sparsity problems. However, these methods only exploited the explicit social relations and almost completely ignored the implicit social relations. In this article, we firstly propose an algorithm to extract the implicit relation in the undirected graphs of social networks by exploiting the link prediction techniques. Furthermore, we propose a new probabilistic matrix factorization method to alleviate the data sparsity problem through incorporating explicit friendship and implicit friendship. We evaluate our proposed approach on two real datasets, Last.Fm and Douban. The experimental results show that our method performs much better than the state-of-the-art approaches, which indicates the importance of incorporating implicit social relations in the recommendation process to address the poor prediction accuracy. Public Library of Science 2016-05-06 /pmc/articles/PMC4859527/ /pubmed/27152663 http://dx.doi.org/10.1371/journal.pone.0154848 Text en © 2016 Reafee et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Reafee, Waleed Salim, Naomie Khan, Atif
spellingShingle	Reafee, Waleed Salim, Naomie Khan, Atif The Power of Implicit Social Relation in Rating Prediction of Social Recommender Systems
author_facet	Reafee, Waleed Salim, Naomie Khan, Atif
author_sort	Reafee, Waleed
title	The Power of Implicit Social Relation in Rating Prediction of Social Recommender Systems
title_short	The Power of Implicit Social Relation in Rating Prediction of Social Recommender Systems
title_full	The Power of Implicit Social Relation in Rating Prediction of Social Recommender Systems
title_fullStr	The Power of Implicit Social Relation in Rating Prediction of Social Recommender Systems
title_full_unstemmed	The Power of Implicit Social Relation in Rating Prediction of Social Recommender Systems
title_sort	power of implicit social relation in rating prediction of social recommender systems
description	The explosive growth of social networks in recent times has presented a powerful source of information to be utilized as an extra source for assisting in the social recommendation problems. The social recommendation methods that are based on probabilistic matrix factorization improved the recommendation accuracy and partly solved the cold-start and data sparsity problems. However, these methods only exploited the explicit social relations and almost completely ignored the implicit social relations. In this article, we firstly propose an algorithm to extract the implicit relation in the undirected graphs of social networks by exploiting the link prediction techniques. Furthermore, we propose a new probabilistic matrix factorization method to alleviate the data sparsity problem through incorporating explicit friendship and implicit friendship. We evaluate our proposed approach on two real datasets, Last.Fm and Douban. The experimental results show that our method performs much better than the state-of-the-art approaches, which indicates the importance of incorporating implicit social relations in the recommendation process to address the poor prediction accuracy.
publisher	Public Library of Science
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4859527/
_version_	1613576622462468096

The Power of Implicit Social Relation in Rating Prediction of Social Recommender Systems

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