2013
DOI: 10.1145/2516891
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Social Link Prediction in Online Social Tagging Systems

Abstract: Social networks have become a popular medium for people to communicate and distribute ideas, content, news, and advertisements. Social content annotation has naturally emerged as a method of categorization and filtering of online information. The unrestricted vocabulary users choose from to annotate content has often lead to an explosion of the size of space in which search is performed. In this article, we propose latent topic models as a principled way of reducing the dimensionality of such data and capturin… Show more

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Cited by 22 publications
(4 citation statements)
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“…Calculating similarities between users or shared contents to infer new links between contents or between users and contents. It's identifying users having similar interests to recommend them their similar preferred contents or identifying similar contents to recommend, per example, inter-contents co-citations (Popescul and Ungar, 2003;Parimi and Caragea, 2011;Rawashdeh et al, 2013;Schifanella et al, 2010;Aiello et al, 2012;Chelmis and Prasanna, 2013).…”
Section: Exploiting Contents or Interest's Semantic Similaritymentioning
confidence: 99%
“…Calculating similarities between users or shared contents to infer new links between contents or between users and contents. It's identifying users having similar interests to recommend them their similar preferred contents or identifying similar contents to recommend, per example, inter-contents co-citations (Popescul and Ungar, 2003;Parimi and Caragea, 2011;Rawashdeh et al, 2013;Schifanella et al, 2010;Aiello et al, 2012;Chelmis and Prasanna, 2013).…”
Section: Exploiting Contents or Interest's Semantic Similaritymentioning
confidence: 99%
“…Within these three categories, we lay out the details and provide a comparative analysis of existing methods in terms of their representation power, flexibility, resource needs and scalability. Specifically, in this session, we elaborate on how previous studies have used different techniques such as collaborative filtering [2,11], topic modeling [11,20], link prediction [5,20], graph-based methods [7,19], Semantic Web technologies [8,12,21] and association rule mining [18] to construct a given type of interest profile for users (e.g. implicit interest profile).…”
Section: Tutorial Outlinementioning
confidence: 99%
“…As a result, Javari and Jalili 20 dug and analyzed people’s links (positive and negative) from social network applications to produce clusters for use in user-based CF. Chelmis and Prasanna 21 used social tags or tagging behaviors to improve the quality of recommendations. Gao et al.…”
Section: Related Workmentioning
confidence: 99%