A recommender mechanism based on case-based reasoning

Authors:
Chen-Shu Wang;Heng-Li Yang
Affiliations:
Graduate Institute of Information and Logistics Management, National Taipei University of Technology, 1, Sec. 3, Chung-hsiao E. Road, Taipei, Taiwan;Department of MIS, National ChengChi University, 64, Sec. 2, Chihnan Road, Taipei, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 14
Cited 2

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Case-Based Reasoning: Concepts, Features and Soft Computing

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Two stages of case-based reasoning - Integrating genetic algorithm with data mining mechanism

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Recommender system for software project planning one application of revised CBR algorithm

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Using case-based reasoning to diagnostic screening of children with developmental delay

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Improving user experience with case-based reasoning systems using text mining and Web 2.0

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Abstract

Case-based reasoning (CBR) algorithm is particularly suitable for solving ill-defined and unstructured decision-making problems in many different areas. The traditional CBR algorithm, however, is inappropriate to deal with complicated problems and therefore needs to be further revised. This study thus proposes a next-generation CBR (GCBR) model and algorithm. GCBR presents as a new problem-solving paradigm that is a case-based recommender mechanism for assisting decision making. GCBR can resolve decision-making problems by using hierarchical criteria architecture (HCA) problem representation which involves multiple decision objectives on each level of hierarchical, multiple-level decision criteria, thereby enables decision makers to identify problems more precisely. Additionally, the proposed GCBR can also provide decision makers with series of cases in support of these multiple decision-making stages. GCBR furthermore employs a genetic algorithm in its implementation in order to reduce the effort involved in case evaluation. This study found experimentally that using GCBR for making travel-planning recommendations involved approximately 80% effort than traditional CBR, and therefore concluded that GCBR should be the next generation of case-based reasoning algorithms and can be applied to actual case-based recommender mechanism implementation.