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    政大典藏 > College of Commerce > Department of MIS > Theses >  Item 140.119/31085


    Please use this identifier to cite or link to this item: https://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
    王貞淑
    Wang, Chen Shu
    Keywords: 推薦機制
    個案推論法
    人工智慧技術整併
    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。
    首先，本研究定義了改良後的推薦機制所應支援的功能，包括：階層式條件的多維度推薦以及多階段的推薦。多維度推薦機制能夠讓使用者從不同的面向去看待決策問題，而階層式條件則允許使用者針對每個維度再往下設定階層式條件，幫助決策者更貼切的描述所遭遇的問題，如此一來推薦機制所提供的推論結果才能更符合決策者的原意。而多階段推薦則是協助決策者進行一連串的規劃方案，而這樣的推薦結論能夠提供可行方案的遠景，讓決策者能夠預先為可能發生的狀況進行準備，進而深化決策者對目前推薦結論的信心。
    除了力求每個(或多個)階段推薦結論的正確性，推薦系統也要與所有的決策階段緊密結合（不僅止於資料搜集階段），所以必須能夠提供決策者行為面的建議，確切的建議決策者應該採取的行動。確切的行為面資訊推薦結論對於決策活動的參考價值更高。
    所以，本研究修改了傳統的案例推導法(CBR)，試圖讓傳統CBR演算法成為符合改良後個案推薦機制的規範，因為CBR演算法最符合人類求解問題的邏輯程序，因此本研究在改良式個案推薦機制中重現CBR演算法中的4R推理循環。而且為了真正落實修正後的CBR演算法，本研究還結合了基因演算法提出GCBR的概念，幫助改良式個案推薦系統能夠更快速有效的收斂出推薦的結論。
    最後，本研究也預期所提出的推薦機制能夠應用於各種不同的領域，而為了驗證所提出的推薦機制執行效率與可行性，本研究也列舉了數個實驗進行的規範方案。本研究所提出的改良式個案推薦機制核心演算法為一概化模型，能夠求解不同型態的決策問題。
    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
    國立政治大學
    資訊管理研究所
    93356506
    97
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0093356506
    Data Type: thesis
    Appears in Collections:[Department of MIS] Theses

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