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He Z.-Q.,PLA University of Science and Technology | Wu L.-F.,PLA University of Science and Technology | Zhang H.-S.,Institute of Electronic System Engineering | Li H.-B.,PLA University of Science and Technology | Lai H.-G.,PLA University of Science and Technology
Sichuan Daxue Xuebao (Gongcheng Kexue Ban)/Journal of Sichuan University (Engineering Science Edition) | Year: 2011

A novel approach based on semantics was proposed to express and match the security supply-and-demand policy of web service. Through constructing a general security ontology, the definition method and matching algorithm of semantic security supply-and-demand policy for web service were presented, and the matching problem of policy was translated into the subsumption reasoning problem of semantic concept. Both the theoretical analysis and experimental evaluation showed that the proposed approach can present the necessary semantic information in the representation of policy and effectively improve the accuracy of matching result, thus overcomes the deficiency of the syntactic approaches. This approach can also simplify the definition and administration of the policy at the same time, which thereby provides a more effective solution for the expression and matching problem of security policy in web service environment. Source


Zhang H.,PLA University of Science and Technology | Gan W.,PLA University of Science and Technology | Xu F.,Institute of Electronic System Engineering
Proceedings - 9th Web Information Systems and Applications Conference, WISA 2012 | Year: 2012

We study the integration of individuals attributes to infer their influence ability in social network in this paper. The influence between individuals is usually asymmetric and can propagate via edges gradually. We suggest an Influence Factor Graph(IFG) which can integrate different node and edge features into a uniform inferring model. And for each node the model can compute personalized influence ability value. Experiment results in Zarchary and Wikipedia co-editing social networks show that, the model can depict influence reasonably and reveal some interesting social influence rules. © 2012 IEEE. Source


Li D.,Institute of Electronic System Engineering
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

The success of a search engine in cloud computing environment relies on the numbers of users and their click-through. If we take the previous search key words as tags of users to study and differentiate the user interaction behaviors, the search engine is able to actively push related and useful information to users based on their previous actions instead of passively waiting for users' queries. However the user searching behavior is affected by lots of factors, and it is quite complex and uncertain. The log files provided by a search engine have recorded all the information of the user interaction process on their servers or browsers, such as key words, click-through rate, time stamp, time on page, IP address, browser type and system stats, even the user location etc, which are all important information to understand and categorize users' searching behavior. Is there any statistical property almost independent to search key words? How to push recommendation based on the queried key words? And how to extract user behavior models of searching actions in order to recommend the information to meet users' real needs more timely and precisely? © 2011 Springer-Verlag Berlin Heidelberg. Source


Chen J.,Beihang University | Liu Y.,Beihang University | Li D.,Institute of Electronic System Engineering
International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems | Year: 2015

The recommender systems community is paying great attention to diversity as key qualities beyond accuracy in real recommendation scenarios. Multifarious diversity-increasing approaches have been developed to enhance recommendation diversity in the related literature while making personalized recommendations to users. In this work, we present Gaussian Cloud Recommendation Algorithm (GCRA), a novel method designed to balance accuracy and diversity personalized top-N recommendation lists in order to capture the user's complete spectrum of tastes. Our proposed algorithm does not require semantic information. Meanwhile we propose a unified framework to extend the traditional CF algorithms via utilizing GCRA for improving the recommendation system performance. Our work builds upon prior research on recommender systems. Though being detrimental to average accuracy, we show that our method can capture the user's complete spectrum of interests. Systematic experiments on three real-world data sets have demonstrated the effectiveness of our proposed approach in learning both accuracy and diversity. © 2015 World Scientific Publishing Company. Source


Chen J.,Beihang University | Wu Z.,Beihang University | Gao H.,Beihang University | Zhang C.,Beihang University | And 3 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

In this paper, we aim to explore interesting landmark recommendations based on geo-tagged photos for each user. Meanwhile, we also try to answer such a question, i.e., when we want to go sightseeing in a large city such as Beijing, where should we go? To achieve our goal, first, we present a data field clustering method (DFCM). By using DFCM, we can cluster a large-scale geo-tagged web photo collection into groups (or landmarks) by location. And then, we provide more friendly and comprehensive overviews for each landmark. Subsequently, we model the users' dynamical behaviors using the fusion user similarity, which not only captures the overview semantic similarity, but also extract the trajectory similarity and the landmark trajectory similarity. Finally, we propose a personalized landmark recommendation algorithm based on the fusion user similarity. Experimental results show that our proposed approach can obtain a better performance than several state-of-the-art methods. © 2013 Springer-Verlag. Source

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