By Jitao Sang
This publication provides the 1st paradigm of social multimedia computing thoroughly from the consumer point of view. diversified from conventional multimedia and internet multimedia computing that are content-centric, social multimedia computing rises lower than the participatory Web2.0 and is basically user-centric. The aim of this publication is to stress the person think about facilitating potent ideas in the direction of either multimedia content material research, person modeling and customised person companies. complex themes like cross-network social multimedia computing also are brought as extensions and capability instructions alongside this examine line.
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Extra resources for User-centric Social Multimedia Computing
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Most recent work aims to first extract user features and then learn models for user attribute inference. In [22, 39], the authors have analyzed the consistency between user profile and social networking activities, and provided statistical proof for inferring user attributes from user online data. Most of these work tackles the user attribute inference problem by designing attribute-specific features and combining with offthe-shelf classifiers. For example, [14] extended the N-gram based models proposed in [5] with sociolinguistic features and linear SVM model, and show applicability to a variety spoken conversational transcripts and more formal enron email corpus.
Given a collection of Google+ users U , each user u ∈ U corresponds to a two dimensional tuple [Xu , Au ]. Xu = [x1 , . . , x K ], where K is the number of attribute types and xk is the user feature of the kth attribute. Au = [a1 , . . , a K ] denotes the user attribute set. Denote the target attribute as T and the auxiliary attribute as S . The whole attribute set is denoted as A = [S , T ]. Thus, the problem is formally defined as: Relational User Attribute Inference. Given a collection of Google+ users U and attribute set A = [S , T ], the goal of relational user attribute inference is to learn (1) a predictive function f (Xu , S ) → Tu to infer the target attribute label of a user; (2) attribute relation compatibility Ψ (ai , ak ) ∈ R|A |×|A | , where Ψ indicate the compatibility strength of attribute relations.
Reference [28] studied Twitter user attribute detection using a mixture of sociolinguistic features as well as n-gram models. Reference [27] attempted to classify users by employing a large set of aggregate features including profile features, tweeting behavior features, linguistic content features, and social network features. For the task of user modeling, various attributes exist for mining and inference. The first type of attribute is demographic attribute. 2 Related Work 37 the rich online social multimedia activities, such as search query log, tweets, favorite video list, check-in history, to infer users’ age, gender, occupation, etc.
