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e-Business Engineering (ICEBE), 2012 IEEE Ninth International Conference on

Date 9-11 Sept. 2012

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Displaying Results 1 - 25 of 77
  • [Front cover]

    Page(s): C4
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  • [Title page i]

    Page(s): i
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  • [Title page iii]

    Page(s): iii
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  • [Copyright notice]

    Page(s): iv
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  • Table of contents

    Page(s): v - ix
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  • Message from the ICEBE 2012 Chairs

    Page(s): x
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  • Message from the ICEBE 2012 Program Chairs

    Page(s): xi - xii
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  • Organizing Committee

    Page(s): xiii - xiv
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  • Program Committee

    Page(s): xv - xviii
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  • Message from the SOAIC 2012 Workshop Co-chairs

    Page(s): xix
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  • SOAIC 2012 Workshop Co-chairs and Program Committee

    Page(s): xx
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  • Message from the EM2I 2012 Workshop Co-chairs

    Page(s): xxi
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  • EM2I 2012 Workshop Co-chairs and Program Committee

    Page(s): xxii
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  • Message from the SOKMBI 2012 Workshop Co-chairs

    Page(s): xxiii
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  • SOKMBI 2012 Workshop Co-chairs and Program Committee

    Page(s): xxiv
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  • Message from the ASOC 2012 Workshop Chairs

    Page(s): xxv
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  • ASOC 2012 Workshop Chairs and Program Committee

    Page(s): xxvi
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  • Keynotes

    Page(s): xxvii - xxix
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (131 KB)  

    This keynotes discusses the following: The Future of E-commerce; Business Informatics: Research that Matters; and E-Gold? An Overview of Technology used at London 2012. View full abstract»

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  • Probabilistic Top-k Dominating Composite Service Selection

    Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (561 KB) |  | HTML iconHTML  

    A few of works have recently focused on relieving service users of the role played in assigning numeric values to QoS criteria as required in traditional service selection scheme. Towards such goals, skyline services for example have been studied recently by a lot of researchers. However, the size of skyline services sometimes is not easily controlled due to intrinsic attributes of services. Besides, some QoS metrics, such as response time, reliability, and etc., usually suffer from network as well as Internet factors (e.g. network unavailability and Internet disconnection), which contributes to the fact that these QoS metrics may fluctuate during run time. Considering this kind of dynamics of QoS metrics, we in this paper propose to obtain probabilistic top-k dominating composite services with uncertain QoS. More important, the number of obtained services can be easily specified by service users, thus avoiding the case that the size of skyline services is sometimes out of control. The experimental results have investigated the feasibility and effectiveness of our approaches. View full abstract»

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  • Personalized Recommendation Based on Reviews and Ratings Alleviating the Sparsity Problem of Collaborative Filtering

    Page(s): 9 - 16
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (321 KB) |  | HTML iconHTML  

    With the development of e-commerce, shopping on-line is becoming more and more popular. When we need to decide whether to purchase a product or not on line, the opinions of others become important. The convenience of new web technologies enables us to freely express our opinions and reviews for various products we have purchased which leads to a serious problem, information overloading. How to mine these review data to understand customers' preferences and make recommendations is crucial to merchants and researchers. Traditional collaborative filtering (CF) algorithm is one of the most successful recommendation system technologies. The core idea of CF algorithm is to recommend products based on other people who have similar tastes with target users. However, the ability of CF is limited by the sparsity problem, which is very common in reality. The reason derives from the fact that traditional CF method only takes users' ratings into account. In this paper, we propose a new personalized recommendation model, i.e. topic model based collaborative filtering (TMCF) utilizing users' reviews and ratings. We exploit extended LDA model to generate topic allocations for each review and then obtain each user's preference. Moreover, a new metric is designed to measure similarity between users alleviating the sparsity problem to a large extent. Finally, recommendations are made based on similar users' ratings. Experiments on seven data sets indicate better prediction accuracy than other traditional and state-of-the-art methods with substantial improvement in alleviating the sparsity problem. View full abstract»

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  • k*NNCA: A Location Privacy Preserving Method for Semi-honest Mobile Users

    Page(s): 17 - 24
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (485 KB) |  | HTML iconHTML  

    Privacy preservation has recently received considerable attention in location-based services. A large number of location cloaking algorithms have been proposed for protecting the location privacy of mobile users. However, most of existing cloaking approaches assume that mobile users are trusted. And exact locations are required to protect location privacy, which are just the information mobile users want to hide. In this paper, we propose a p-anti-conspiring privacy model to anonymize over semi-honest users. Furthermore, a k*NNG-based cloaking algorithm k*NNCA is proposed to protect the location privacy without exact locations. The preliminary experimental results show the effectiveness of the proposed algorithm. View full abstract»

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  • Research on Trust Evaluation Model for Mobile Commerce Based on Structural Equation Modeling

    Page(s): 25 - 32
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (324 KB) |  | HTML iconHTML  

    Based on in-depth study of trust concept and trust factors, this paper establishes a theory framework of influence factors of consumers' trust in mobile commerce, involving seven first-level factors and twelve second-level factors. A questionnaire on trust evaluation of mobile commerce is designed, and questionnaire data is collected through both online and offline ways. Using the collected data, reliability analysis and validity analysis on the trust evaluation scale are conducted respectively. A first-order confirmatory factor analysis is used to test the established trust evaluation model for mobile commerce. The necessity and feasibility of using second-order factor analysis to determine the weights of trust influence factors are discussed. The processes and results of SEM (Structural Equation Modeling)-based first-order factor analysis and second-order factor analysis using AMOS software are described in detail. View full abstract»

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  • The Impact of Online Channel on the Performance of China's Listed Retailers

    Page(s): 33 - 38
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (260 KB) |  | HTML iconHTML  

    Although the benefits of the Internet as an additional sales channel to traditional retailers are clear, there are significant variations in the scope and depth of online channel use among retailers. Drawing data from listed retail companies in China, this study examines the impact of online channel use on retailers' performance using event study. The results show that the online channel provides significant improvements in sales, cost, inventory, and return on investments. In addition, we find that the timing of online channel adoption does not have a significant impact on performance improvement, but having a higher local store presence does. View full abstract»

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  • An Empirical Study of Customer Loyalty to Internet Banking in China

    Page(s): 39 - 44
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (263 KB) |  | HTML iconHTML  

    The rapid development of information technology and the severe competition of the bank industry make the internet banking become a new service channel for Chinese banks to acquire new customers and to retain the existing customers. Because the larger part of the profit in internet banking is from the existing customers, Chinese banks have been making their great efforts in order to win the market share by means of retaining loyal customers in recent years. Meanwhile, the studies as to the factors affecting the customer loyalty to internet banking are arousing the interest and attention of more and more scholars in China. This study is to try to find the factors affecting customer loyalty in internet banking, i.e., to investigate the roles of trust and satisfaction in customer loyalty to internet banking in China theoretically and empirically. A model is built with customer loyalty as the endogenous variable, with service quality, brand image and perceived value as exogenous variables, and with satisfaction and trust as mediating variables. Based on data from a sample of 429 internet banking customers collected through a field and an online survey, the results find all the causal relationships in the model significant by using structural equation modeling. Theoretical and managerial implications are provided at the end of the study. View full abstract»

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  • A New Selective Clustering Ensemble Algorithm

    Page(s): 45 - 49
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (222 KB) |  | HTML iconHTML  

    Selective clustering ensemble is usually based on the reference partition to select members of the ensemble. General method of generating reference partition is to use preliminary ensemble results, yet it cannot eliminate the influence of the inferior clustering partitions and the final clustering result is not satisfactory. In order to solve this problem, the paper proposes a new selective clustering ensemble algorithm. The new algorithm includes two points :(1) selecting the best reference partition based on clustering validity evaluation, (2)putting forward the new selection strategy and the method of member's weight. The experimental results show that the new algorithm is effective and clustering accuracy could be significantly improved. View full abstract»

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