00544nas a2200145   4500008004100000245010000041210006900141260000900210300001200219490000700231100001100238700001400249700001700263856011800280       2016                        eng d00aA novel recommendation strategy for user-based collaborative filtering in intelligent marketing0 anovel recommendation strategy for userbased collaborative filter  c2016  a81 - 910 v141 aYi, J.1 aZhang, L.1 aPhelan, C.A.  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84974784671&partnerID=40&md5=a48f248084cf977768be0931a9ac4d8902188nas a2200217   4500008004100000245008400041210006900125260000900194300001400203490000700217520149000224653002101714653002301735653001601758653002801774100001301802700001301815700001401828700001101842856011701853       2013                        eng d00aException detection for web service composition using improved bayesian network0 aException detection for web service composition using improved b  c2013  a108 - 1140 v113 aNew application systems generated by composition of web services dynamically have become a development trend in network environment. However, since a variety of external services are invoked with different quality of service during processing, the problem of how to keep the execution stable must be addressed in order to improve the reliability and availability of the combination of services. In this paper, an approach of exception detection is presented for web service composition by improved Bayesian network. Firstly, the topological structure of Bayesian network is established, where the causal relationships between underlying web services are mapped to Bayesian network in service combination process. The conventional Bayesian network algorithm is improved to satisfy the demand of better conversion from nodes in web service composition to nodes in Bayesian network. Secondly, the parameter setting in Bayesian network is explored for determining the prior probability of service nodes and the conditional probability of service output node. Thirdly, the algorithm of the improved exception detection model is introduced, with the aim of locating the web services that cause exceptions in the execution of composite services. Lastly, a case study is given and the results analysis is also conducted. The results show that the presented approach not only considers the uncertainty during exception detection, but also can identify the exception web services in the process.10aBayesian network10aException handling10aWeb service10aWeb service composition1 aChen, Y.1 aYing, S.1 aZhang, L.1 aWu, J.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84879099166&partnerID=40&md5=0973327f022946c84a165872a228552401279nas a2200193   4500008004100000245005000041210004900091260000900140300001400149490000700163520067100170653002400841653002200865653002500887653002900912100001300941700001400954856011700968       2013                        eng d00aInformation sharing in supply chain: A review0 aInformation sharing in supply chain A review  c2013  a125 - 1300 v113 aThe importance of information sharing in supply chain management is a matter of consensus between practitioners and researchers. Information sharing can increase supply chain efficiency by reducing inventories and smoothing production. Studies from different and recognised authors are reviewed, compiled and analyzed in three aspects. First, the types of shared information in supply chain are summarized. After, the value of different shared information and the factors which affect its value are presented. Moreover, the methodologies used in literature are detailed. The paper concludes with some future directions for theory construction and empirical research.10aInformation sharing10aLiterature review10aSupply chain network10aSupply chains management1 aYang, Z.1 aZhang, L.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84879070693&partnerID=40&md5=492576360136fd7384f5e3b18e51a4d102318nas a2200241   4500008004100000245006700041210006700108260000900175300001400184490000700198520158900205653001601794653001701810653002601827653001501853653002801868100001101896700001301907700001201920700001401932700001301946856011701959       2013                        eng d00aRobustness analysis of social network based on a dynamic model0 aRobustness analysis of social network based on a dynamic model  c2013  a453 - 4610 v113 aDue to the high vulnerability of today's social systems against disruptions, it has become critical to measure and manage the robustness of social networks. In this study the relationship between social network's topology and its robustness in the presence of cascading failures is examined. In the attempt to help practice in increasing the robustness of social systems, the concept of the robustness of social systems is discussed and defined first. And then we propose a new dynamic model of social systems to investigate the robustness of social systems. We theorize that network characteristics such as average path length, node degree, and size of the largest connected component in social networks and so on. Based on the model, a good evaluation method is proposed to investigate the robustness of social networks against the cascading failures of component nodes. Finally, the damage degree of social networks considering cascading failures is investigated through our model. The simulation results confirm that there is a positive relationship between the robustness of social systems and social system performance, while a negative relationship between the topology structure and the robustness of social systems. By the dynamic model, we not only identify the potential key nodes, but also obtain the parameter value which can make the social network reach strongest robustness level. Our work develops a new methodology to the real world application. This research is helpful to increase the robustness of such complex systems and tackle the optimization problem of them.10aDisruptions10aGraph Theory10aNetwork Damage Degree10aRobustness10aSocial Network Analysis1 aWu, H.1 aYing, S.1 aJia, X.1 aZhang, L.1 aChen, X.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84892163854&partnerID=40&md5=861e44abb43f2d1f64ca3d9c26ee9c4f01966nas a2200217   4500008004100000245005200041210005200093260000900145300001400154490000700168520130000175653001901475653002101494653002701515653002401542653002801566100001401594700001101608700001201619856011701631       2012                        eng d00aWord combination kernel for text categorization0 aWord combination kernel for text categorization  c2012  a202 - 2110 v103 aWe proposed a novel kernel for text categorization. This kernel is an inner product in the feature space generated by all word combinations of specified length. A word combination is a collection of different words co-occurring in the same sentence. The word combination of length k is weighted by the k-th root of the product of the inverse document frequencies (IDF) of its words. A computationally simple and efficient algorithm was proposed to calculate this kernel. By restricting the words of a word combination to the same sentence and considering multi-word combinations, the word combination features can capture similarity at a more specific level than single words. By discarding word order, the word combination features are more compatible with the flexibility of natural language and the dimensionality this kernel can be reduced significantly compared to the word-sequence kernel. We conducted a series of experiments on the Reuters-21578 dataset and 20 Newsgroups dataset. This kernel consistently achieves better performance than the classical word kernel and word-sequence kernel on the two datasets. We also assessed the impact of word combination length on performance and compared the computing efficiency of this kernel to those of the word kernel and word-sequence kernel.10aKernel methods10aMachine learning10aSupport vector machine10aText classification10aWord-combination kernel1 aZhang, L.1 aHu, X.1 aQin, S.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84866439932&partnerID=40&md5=8649dfc8c1ee23ca70f2b137a74a19e4