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 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