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Facebook’s public social interaction utilization to assist recommendation across system domain

Social media is most prominent internet transition for this decade and Facebook holds its largest share. Facebook has been utilized by researchers from different perspectives e.g. opinion mining, user mood swing pattern, influential person identification etc. whereas recently Facebook’s social inter...

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Main Authors:	Khan, M. M., Ghani, I., Jeong, S. R., Ibrahim, R., Qureshi, K. N.
Format:	Article
Published:	Asian Research Publishing Network 2016
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.utm.my/id/eprint/72394/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978414707&partnerID=40&md5=83fedb7f845b8bc9f983eee0909dc1e7
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id	my.utm.72394
record_format	eprints
spelling	my.utm.723942017-11-20T08:23:44Z http://eprints.utm.my/id/eprint/72394/ Facebook’s public social interaction utilization to assist recommendation across system domain Khan, M. M. Ghani, I. Jeong, S. R. Ibrahim, R. Qureshi, K. N. QA75 Electronic computers. Computer science Social media is most prominent internet transition for this decade and Facebook holds its largest share. Facebook has been utilized by researchers from different perspectives e.g. opinion mining, user mood swing pattern, influential person identification etc. whereas recently Facebook’s social interactions were used for recommendation purposes. Although social interactions assisted recommendation, these interactions forced algorithm to work inside Facebook’s ecosystem i.e. recommending items existing inside Facebook to Facebook users and these interactions were private in nature, requiring explicit permission from user before algorithm execution. This study utilize Facebook’s public social interactions to recommend items across system domain i.e. recommending items to users existing outside Facebook. For this purpose we propose an algorithm that first identify items on Facebook’s public pages, gather social interactions related to them, generate a rank list and finally recommend it to external users. As an experimental case study, “whatmobile.pk” Facebook’s public social page was scanned for items and respective social interactions. These items were then compared with “fan” attribute of items existing on GSMARENA.com website in order to show rank similarity. 299 total items were found common between Facebook’s public page and GSMARENA website. Items were ranked according to social interactions and “fans” quantity. Then a positive spearman correlation of 0.547 was found which was improved to 0.660 by excluding 22 mobile phones. Asian Research Publishing Network 2016 Article PeerReviewed Khan, M. M. and Ghani, I. and Jeong, S. R. and Ibrahim, R. and Qureshi, K. N. (2016) Facebook’s public social interaction utilization to assist recommendation across system domain. Journal of Theoretical and Applied Information Technology, 88 (3). pp. 392-397. ISSN 1992-8645 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978414707&partnerID=40&md5=83fedb7f845b8bc9f983eee0909dc1e7
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. Ghani, I. Jeong, S. R. Ibrahim, R. Qureshi, K. N. Facebook’s public social interaction utilization to assist recommendation across system domain
description	Social media is most prominent internet transition for this decade and Facebook holds its largest share. Facebook has been utilized by researchers from different perspectives e.g. opinion mining, user mood swing pattern, influential person identification etc. whereas recently Facebook’s social interactions were used for recommendation purposes. Although social interactions assisted recommendation, these interactions forced algorithm to work inside Facebook’s ecosystem i.e. recommending items existing inside Facebook to Facebook users and these interactions were private in nature, requiring explicit permission from user before algorithm execution. This study utilize Facebook’s public social interactions to recommend items across system domain i.e. recommending items to users existing outside Facebook. For this purpose we propose an algorithm that first identify items on Facebook’s public pages, gather social interactions related to them, generate a rank list and finally recommend it to external users. As an experimental case study, “whatmobile.pk” Facebook’s public social page was scanned for items and respective social interactions. These items were then compared with “fan” attribute of items existing on GSMARENA.com website in order to show rank similarity. 299 total items were found common between Facebook’s public page and GSMARENA website. Items were ranked according to social interactions and “fans” quantity. Then a positive spearman correlation of 0.547 was found which was improved to 0.660 by excluding 22 mobile phones.
format	Article
author	Khan, M. M. Ghani, I. Jeong, S. R. Ibrahim, R. Qureshi, K. N.
author_facet	Khan, M. M. Ghani, I. Jeong, S. R. Ibrahim, R. Qureshi, K. N.
author_sort	Khan, M. M.
title	Facebook’s public social interaction utilization to assist recommendation across system domain
title_short	Facebook’s public social interaction utilization to assist recommendation across system domain
title_full	Facebook’s public social interaction utilization to assist recommendation across system domain
title_fullStr	Facebook’s public social interaction utilization to assist recommendation across system domain
title_full_unstemmed	Facebook’s public social interaction utilization to assist recommendation across system domain
title_sort	facebook’s public social interaction utilization to assist recommendation across system domain
publisher	Asian Research Publishing Network
publishDate	2016
url	http://eprints.utm.my/id/eprint/72394/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978414707&partnerID=40&md5=83fedb7f845b8bc9f983eee0909dc1e7
_version_	1643656429563805696
score	13.211869

Facebook’s public social interaction utilization to assist recommendation across system domain

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