Abstract:
Information overload on the Web has created enormous challenges to customers selecting products for online purchases and to online businesses attempting to identify customer's preferences efficiently. Various recommender systems employing different data representations and recommendation methods are currently used to address these challenges. In this research, we developed a graph model that provides a generic data representation and can support different recommendation methods. To demonstrate its usefulness and flexibility, we developed three recommendation methods: direct retrieval, association mining, and high-degree association retrieval. We used a data set from an online bookstore as our research test-bed. Evaluation results showed that combining product content information and historical customer transaction information achieved more accurate predictions and relevant recommendations than using only collaborative information. However, comparisons among different methods showed that high-degree association retrieval did not perform significantly better than the association mining method or the direct retrieval method in our test-bed.
Abstract:
Identity management is critical to various governmental practices ranging from providing citizens services to enforcing homeland security. The task of searching for a specific identity is difficult because multiple identity representations may exist due to issues related to unintentional errors and intentional deception. We propose a Naïve Bayes identity matching model that improves existing techniques in terms of effectiveness. Experiments show that our proposed model performs significantly better than the exact-match based technique and achieves higher precision than the record comparison technique, In addition, our model greatly reduces the efforts of manually labeling training instances by employing a semi-supervised learning approach. This training method outperforms both fully supervised and unsupervised learning. With a training dataset that only contains 30% labeled instances, our model achieves a performance comparable to that of a fully supervised learning. © Springer-Verlag Berlin Heidelberg 2006.
Abstract:
The relationships between phenomena observed in the real world and their representations in virtual contexts have generated interest among researchers. In particular, the manifestations of social movements in virtual environments have been examined, with many studies dedicated to the analysis of the virtual linkages between groups. In this research, a form of link analysis was performed to examine the relationship between virtual linkage intensity and real world physical proximity among the social movement groups identified in the Southern Poverty Law Center Spring 2009 Intelligence Report. Findings indicate the existence of significant relationships between virtual linkage intensity and physical proximity, distinctive to various ideological categorizations. The results provide valuable insights into the behaviors of social movements in virtual environments. © 2010 IEEE.
Abstract:
Contents in extremist online forums are invaluable data sources for extremism reseach. In this study, we propose a systematic Web mining approach to collecting and monitoring extremist forums. Our proposed approach identifies extremist forums from various resources, addresses practical issues faced by researchers and experts in the extremist forum collection process. Such collection provides a foundation for quantitative forum analysis. Using the proposed approach, we created a collection of 110 U.S. domestic extremist forums containing more than 640,000 documents. The collection building results demonstrate the effectiveness and feasibility of our approach. Furthermore, the extremist forum collection we created could serve as an invaluable data source to enable a better understanding of the extremism movements. © Springer-Verlag Berlin Heidelberg 2006.
Abstract:
Innovation is one of the primary characteristics that separates successful from unsuccessful organizations. Organizations have a choice in selecting knowledge that is recombined to produce new innovations. The selection of knowledge is influenced by the status of inventors in an organization's internal knowledge network. In this study, we model knowledge flow within an organization and contend that it exhibits unique characteristics not incorporated in most social network measures. Using the model, we also propose a new measure based on random walks and team identification and use it to examine innovation selection in a large organization. Using empirical methods, we find that inventor status determined by the new measure had a significant positive relationship with the likelihood that his/her knowledge would be selected for recombination. We believe that the new measure in addition to modeling knowledge flow in a scientific collaboration network helps better understand how innovation evolves within organizations. © 2009 IEEE.
