Zhi-Ang Wu

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Web service recommendation has become a hot yet fundamental research topic in service computing. The most popular technique is the Collaborative Filtering (CF) based on a user-item matrix. However, it cannot well capture the relationship between Web services and providers. To address this issue, we first design a cube model to explicitly describe the(More)
Collaborative filtering (CF) technique is capable of generating personalized recommendations. However, the recommender systems utilizing CF as their key algorithms are vulnerable to shilling attacks which insert malicious user profiles into the systems to push or nuke the reputations of targeted items. There are only a small number of labeled users in most(More)
How to guarantee user’s QoS (Quality of Service) demands become increasingly important in a service-oriented grid environment. Current research on grid resource advance reservation, a well known and effective mechanism to guarantee QoS, fails to adapt to dynamic behavior of grid, and cannot solve imprecise deny of reservation request problem efficiently.(More)
In this paper, we identify and solve a multi-join optimization problem for Arbitrary Feature-based social image Similarity JOINs(AFS-JOIN). Given two collections(i.e., R and S) of social images that carry both visual, spatial and textual(i.e., tag) information, the multiple joins based on arbitrary features retrieves the pairs of images that are visually,(More)
Discovering communities can promote the understanding of the structure, function and evolution in various systems. Overlapping community detection in poly-relational networks has gained much more interests in recent years, due to the fact that poly-relational networks and communities with pervasive overlap are prevalent in the real world. A plethora of(More)
Community detection is a classic and very difficult task in complex network analysis. As the increasingly explosion of social media, scaling community detection methods to large networks has attracted considerable recent interests. In this paper, we propose a novel SIMPLifying and Ensembling (SIMPLE) framework for parallel community detection. It employs(More)
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