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Large-scale graph computation is central to applications ranging from language processing to social networks. However, natural graphs tend to have skewed power-law distributions where a small subset of the vertices have a large number of neighbors. Existing graph-parallel systems suffer from load imbalance, high communication cost, or suboptimal and complex processing. In this paper we present GraphA,...
Collaborative Filtering (CF) is widely applied to personalized recommendation systems. Traditional collaborative filtering techniques make predictions through a user-item matrix of ratings which explicitly presents user preference. With the increasingly growing number of users and items, insufficient rating data still leads to the decreasing predictive accuracy with traditional collaborative filtering...
The popularity of smart phones and other intelligent devices has made it possible for us to organize social activities by means of opportunistic social networks. However, message dissemination performance in opportunistic social networks is highly dependent on the cooperation between different users. In this paper, we put forward the model of markets with intermediaries as an incentive mechanism....
As hundreds of millions of users access online social communities at daily or even real-time basis, large amounts of user data are continuously generated. The fast-growing user-generated content poses great challenges on online social network system design for efficient content management and delivery. For instance, in content-centric online social network, all contents are organized in a temporal...
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