@article {534,
	title = {A novel recommendation strategy for user-based collaborative filtering in intelligent marketing},
	journal = {Journal of Digital Information Management},
	volume = {14},
	year = {2016},
	note = {Export Date: 22 February 2018},
	month = {2016},
	pages = {81 - 91},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84974784671\&partnerID=40\&md5=a48f248084cf977768be0931a9ac4d89},
	author = {Yi, J. and Zhang, L. and Phelan, C.A.}
}
@article {378,
	title = {Exception detection for web service composition using improved bayesian network},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {108 - 114},
	abstract = {New 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.},
	keywords = {Bayesian network, Exception handling, Web service, Web service composition},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84879099166\&partnerID=40\&md5=0973327f022946c84a165872a2285524},
	author = {Chen, Y. and Ying, S. and Zhang, L. and Wu, J.}
}
@article {377,
	title = {Information sharing in supply chain: A review},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {125 - 130},
	abstract = {The 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.},
	keywords = {Information sharing, Literature review, Supply chain network, Supply chains management},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84879070693\&partnerID=40\&md5=492576360136fd7384f5e3b18e51a4d1},
	author = {Yang, Z. and Zhang, L.}
}
@article {426,
	title = {Robustness analysis of social network based on a dynamic model},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {453 - 461},
	abstract = {Due to the high vulnerability of today{\textquoteright}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{\textquoteright}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.},
	keywords = {Disruptions, Graph Theory, Network Damage Degree, Robustness, Social Network Analysis},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84892163854\&partnerID=40\&md5=861e44abb43f2d1f64ca3d9c26ee9c4f},
	author = {Wu, H. and Ying, S. and Jia, X. and Zhang, L. and Chen, X.}
}
@article {333,
	title = {Word combination kernel for text categorization},
	journal = {Journal of Digital Information Management},
	volume = {10},
	year = {2012},
	note = {Export Date: 10 July 2014},
	month = {2012},
	pages = {202 - 211},
	abstract = {We 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.},
	keywords = {Kernel methods, Machine learning, Support vector machine, Text classification, Word-combination kernel},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84866439932\&partnerID=40\&md5=8649dfc8c1ee23ca70f2b137a74a19e4},
	author = {Zhang, L. and Hu, X. and Qin, S.}
}
