Predicting User-to-content Links in Flickr Groups
Citations
53 citations
Cites background from "Predicting User-to-content Links in..."
...…social networking websites or services—such as Facebook, Flickr, Google+, LinkedIn, Twitter, and Weibo (Chang et al. 2013; Gonzalez et al. 2013; Negi and Chaudhury 2012; Paul et al. 2012; Sumbaly, Kreps, and Shah 2013; Sun et al. 2013; van Laere, Schockaert, and Dhoedt 2013)—are in use…...
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4 citations
1 citations
Cites background from "Predicting User-to-content Links in..."
...Even paper [8] has predicted the formation of user-to-content links in Flickr Groups to predict the chance that a user will comment or like an image updated by another user....
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References
262 citations
"Predicting User-to-content Links in..." refers background in this paper
...Recently, Social Recommender Systems [22] [23] have been proposed that utilize the social network information (between users) to improve recommendations....
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181 citations
"Predicting User-to-content Links in..." refers background or methods in this paper
...Some noteworthy examples are the work done by [11], [12], [13] and [14]....
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...[12] describe a method to independently assess topical alignment and the creation of social links, thereby showing that similarity in user interests is indeed independently driving the creation of new social links....
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...[12] and [13] have independently looked at social and topical alignment within groups....
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163 citations
"Predicting User-to-content Links in..." refers background in this paper
...The Flickr network has also attracted a fair amount of attention in the social network research community - ([8],[1],[9]) studied the structure and the temporal evolution of the Flickr social network, ([10]) investigated the effect of social networks on content popularity and content dissemination etc....
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154 citations
"Predicting User-to-content Links in..." refers background in this paper
...Flickr has been the object of many studies, including attempts to characterize users ([2], [3]), the tags users assign to photos ([4]), investigating user’s motivations for publishing and tagging ([5], [6]), automatically assigning geographic coordinates to Flickr photos ([7]) etc....
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136 citations
"Predicting User-to-content Links in..." refers methods in this paper
...The subgroup-membership vectors πv , πu for both target and source user are obtained from the TMMSB model as explained in III-B....
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...The TMMSB takes into account the frequency of interactions amongsts user when discovering community/subgroups and assigning users to these community/subgroups....
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...The MMSB model treats the network as a group of users and a list of binary relations between users (i.e. presence or absence of an edge indicates the presence or absence of an interaction)....
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...To over come this limitation the Mixed Membership Stochastic Block Model (MMSB) [26] was proposed which allows each user to be associated with multiple subgroups rather than with just a single subgroup, via a membership probabilitylike vector....
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...To accurately model our interaction network and the subgroups in it we thus use a variant of the MMSB model called the Transactional Mixed Membership Stochastic Block model or TMMSB [27]....
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