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  • × author_ss:"Chen, A.-P."
  1. Chen, C.-C.; Chen, A.-P.: Using data mining technology to provide a recommendation service in the digital library (2007) 0.00 
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    Abstract
    Purpose - Since library storage has been increasing day by day, it is difficult for readers to find the books which interest them as well as representative booklists. How to utilize meaningful information effectively to improve the service quality of the digital library appears to be very important. The purpose of this paper is to provide a recommendation system architecture to promote digital library services in electronic libraries. Design/methodology/approach - In the proposed architecture, a two-phase data mining process used by association rule and clustering methods is designed to generate a recommendation system. The process considers not only the relationship of a cluster of users but also the associations among the information accessed. Findings - The process considered not only the relationship of a cluster of users but also the associations among the information accessed. With the advanced filter, the recommendation supported by the proposed system architecture would be closely served to meet users' needs. Originality/value - This paper not only constructs a recommendation service for readers to search books from the web but takes the initiative in finding the most suitable books for readers as well. Furthermore, library managers are expected to purchase core and hot books from a limited budget to maintain and satisfy the requirements of readers along with promoting digital library services.
    Type
    a
  2. Chen, A.-P.; Chen, M.-Y.: ¬A review of survey research in knowledge management performance (2005) 0.00 
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    Abstract
    This paper surveys knowledge management (KM) development using a literature review and classification of articles from 1995 to 2004 with a keyword index and article abstract in order to explore how KM performance evaluation has developed during this period. Based on the scope of 76 articles from 78 academic journals of KM, this paper surveys and classifies KM measurements using the following eight categories: qualitative analysis, quantitative analysis, financial indicator analysis, non-financial indicator analysis, internal performance analysis, external performance analysis, project-oriented analysis, and organizational-oriented analysis together with their measurement matrices for different research and problem domains. Discussion is presented, indicating the followings future development directions for KM performance evaluation: (1) KM performance evaluation is getting more important. (2) The quantitative analysis is the primary methodology in KM performance evaluation. (3) Firms are now highlighting the KM performance of competitors, through benchmarking or best practices, rather than internally auditing KM performance via balanced scorecard. (4) Firms may begin to focus more on project management measurement, than on the entire organization.
    Type
    a

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