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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/31085
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/31085

    Title: 改良式個案推薦機制: 階層式擷取條件與階段式的個案推理演算法
    Enhanced Case-Based Recommender Mechanism:Hierarchical Case-Retrieved Criteria and Multiple-Stage CBR Algorithm
    Authors: 王貞淑
    Wang, Chen Shu
    Contributors: 楊亨利
    Yang, Heng Li
    Wang, Chen Shu
    Keywords: 推薦機制
    Date: 2008
    Issue Date: 2009-09-14 09:13:35 (UTC+8)
    Abstract: 各類電子商務網站上的推薦機制應用已日趨廣泛且成熟。而隨著決策問題日漸複雜,現行的推薦機制發展已經可以看到應用的界限,再也無法貼近使用者所面臨的複雜問題。現行的推薦機制架構需要被重新審視、定義與設計其核心演算法。本研究用更寬廣的角度看待推薦機制,並將改良後的推薦機制視為解決問題的新典範。
    Recommender system can be regarded as fundamental technology of electronic commence web site. Some researchers also claimed that recommender system push the electronic web site to another development peak. Recommender system would need some mechanisms. These recommender mechanisms should be reviewed, redefined and expanded to include particularly case-based mechanism that focus on reality problem solving.
    Recently, CBR applications had been extended to provide recommendation mechanism based on previous cases. The abstract recommendation problems are usually hard to be formulated in strict mathematic models, and often solved via word-mouse experience. Case-Based Reasoning (CBR) is a paradigm, concept and instinctive mechanism for ill-defined and unstructured problem solving. Similarly to human problem solving process, CBR retrieves past experiences to reuse for target problem. Of course, the solutions of past cases may need to be revised for applying. The successful problem-solving experiences are then retained for further reusing. These are well-known 4R processes (retrieve, reuse, revise, and retain) of traditional CBR.
    Nevertheless, the case-based recommender mechanism is particularly suitable for reality problem reference because case-style can be used to describe unstructured problem. The next generation recommender mechanism should focus on the real life problem solving and applications. Thus, case-based recommender mechanism can be regarded as a new problem solving paradigm.
    To enhance traditional CBR algorithm to case-based recommender mechanism, the original CBR should be redesigned. In the traditional CBR algorithm, based on multiple objectives, the retrieved cases could provide to decision maker for references. However, as the decision problem is getting complex, pure multiple objective problem representation is too unsophisticated to reflect reality. Thus, a revised CBR algorithm equipped with capability to deal with more complexity is needed. Additionally, decision makers would wish to achieve the actionable information. The existing recommender mechanism can not provide the actionable direction to decision maker. Based on previous cases provided by CBR, decision maker would further hope that recommender mechanism could tell them how to do. These capabilities should be included into traditional CBR algorithm.
    Furthermore, traditional CBR has to evaluate all cases in case base to return the most similar case(s). The efficiency of CBR is obviously negatively related to the size of case base. Thus, a number of approaches have devoted to decrease the effort for case evaluation. This research proposes a revised CBR mechanism, named GCBR, which can be regarded as next generation CBR algorithm. GCBR can be applied to reality applications, particularly case-based recommender mechanism. Thus, it can be treated as a new problem solving paradigm. It also intends to improve traditional CBR efficiency stability no matter what kinds of case representation and indexing approaches.
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    Description: 博士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0093356506
    Data Type: thesis
    Appears in Collections:[資訊管理學系] 學位論文

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