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題名 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
作者 王貞淑
Wang, Chen Shu
貢獻者 楊亨利
Yang, Heng Li
王貞淑
Wang, Chen Shu
關鍵詞 推薦機制
個案推論法
人工智慧技術整併
日期 2008
上傳時間 14-Sep-2009 09:13:35 (UTC+8)
摘要 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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5. Cao, L. and Zhang, C., (2007) “Domain-Driven Data Mining: A Framework,” IEEE intelligent System, Vol. 22, No.4, pp. 78-79.
6. Chang, C.L., (2005) “Using Case-Based Reasoning to Diagnostic Screening of Children with Developmental Delay,” Expert Systems with Applications, Vol. 28, pp. 237-247.
7. Chang, P.C. and Lai, C.Y., (2005) “A Hybrid System Combining Self-Organizing Maps with Case-Based Reasoning in Wholesaler’s New-Release Book for Forecasting,” Expert Systems with Application, Vol. 29, pp. 183-192.
8. Changchien, S.W. and Lin, M.C., (2005) “Design and Implementation of a Case-Based Reasoning System for Marketing Plans,” Expert Systems with Applications, Vol. 28, pp. 43-53.
9. Cheng, Y.S.J. and Cheng, K.P.S., (2004) “Case-Based Reasoning System for Predicting Yarn Tenacity,” Textile Research Journal, Vol. 74, No. 8, pp. 718-722.
10. Choobineh, J. and Lo. A.W., (2005/2006) “Should Rule-Based Reasoning Be Enhanced By Case-Based Reasoning for Conceptual Database Design? A Theory and an Experiment,” The Journal of Computer Information Systems, Vol. 46, No. 2, pp. 69-77.
11. Choy, K.L. and Lee, W.B., (2002) “A Generic Tool for the Selection and Management of Supplier Relationships in an Outsourced Manufacturing Environment: the Application of Case Based Reasoning,” Logistics Information Management, Vol. 15, No. 4, pp. 235-253.
12. Cirovic, G. and Cekic, Z., (2002) “Case-Based Reasoning Model Applied as a Decision Support for Construction Projects,” Kybernete, Vol. 31, No. 5/6, pp. 896-908.
13. Coello, C.A., (2000) “An updated survey of GA-Based Multiobjective Optimization Techniques,” ACM Computing Surveys, Vol. 32, No. 2, pp. 109-143.
14. Diakoulaki, D., Mavrotas, G., and Papayannakis, L., (1995) “Determining Objective Weights in Multiple Criteria Problems: The Critic Method,” Computers and Operations Research, Vol. 22, No. 7, pp. 763-770.
15. Dolog, P. and Sintek, M., (2004) “Personalization in Distributed e-Learning Environments,” Proceedings of the 2004 WWW conference. pp. 170-179.
16. Donzellli, P., “A Decision Support System for Software Project Management,” IEEE Software, July/August, 2006, 67-75.
17. Dweiri, F.T., Kablan, M.M., “Using Fuzzy Decision Making for the Evaluation of the Project Management Internal Efficiency,” Decision Support Systems, Vol. 42, 2006, 712-726.
18. Felfernig, A., and Friedrich, G., (2007) “Recommender Systems”, IEEE Intelligent System, Vol. 22, No. 3, pp. 18-21.
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20. Goldberg, D.E., (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addision-Wesley.
21. Golobardes, E., Llora, X., Salamo, M. and Marti, J., “Computer Aided Diagnosis with Case-Based Reasoning and Genetic Algorithms,” Knowledge-Based Systems, Vol. 15, 2002, 45-52.
22. Garcia, M., Roman, I.R., Penalvo, F., and bonilla, M., (2008) “An Association Rule Mining Method for Estimating the Impact of Project Management Policies on Software Quality, Development Time and Effort,” Expert Systems with Applications, Vol. 34, 522-529.
23. Guiu, J.M., Ribe, E.G., Mansilla, E.B. and Fabrega, X.L., (1999) “Automatic Diagnosis with Genetic Algorithms and Case-Based Reasoning,” Artificial Intelligence in Engineering, Vol. 13, pp. 367-372.
24. Han, W.M., and Huang, S.J., (2007) “An Empirical Analysis of Risk Components and performance on Software project,” The Journal of System and Software, Vol. 80, 42-50.
25. Holland, J.H., Adaptation in Natural and Artifical Systems. Ann Arbor, MI: The University of Michigan Press.
26. Juan, Y.K., Shin, S.G. and Perng, Y.H., (2006) “Decision Support for Housing Customization: A Hybrid Approach Using Case-Based Reasoning and Genetic Algorithm,” Expert Systems with Application, Vol. 31, pp. 83-93.
27. Lee, H.Y., Ahn, H., and Han, I., (2007) “VCR: Virtual Community Recommender Using the Technology Acceptance Model and the User’s Needs Type,” Expert Systems with Applications, Vol. 33, pp. 984-995.
28. Lee, J., and Lee, N., “Least Modification principle for Case-based Reasoning: a Software Project Planning Experience,” Expert Systems with Applications, Vol. 30, 2006, 190-202
29. Linden, G., Smith, B., and York, J., (2003) “Amazon.com Recommendations: Item-to-Item Collaborative Filtering,” IEEE Internet Computing, Jan/Feb.
30. Love, E.D., and Irani, Z., “A Project Management Quality Cost Information System for the Construction Industry,” Information & Management, Vol. 40, 2003, 649-661.
31. Macauley, M., Palmer, K. and Shin, J.S. (2003), “Dealing with Electronic Waste： Modeling the Costs and Environmental Benefits of Computer Monitor Disposal,” Journal of Environmental Management, Vol. 68, pp. 13-22.
32. Mahaney, R., Lederer, A., “Information Systems Project Management: an Agency Theory Interpretation,” Journal of Systems and Software, Vol. 68, 2008, 1-9.
33. Marling, C., Sqalli, M. and Rissland, E., Hector, M.A., and Aha, D., (2002) “Case-Based Reasoning Integrations,” AI Magazine, Vol. 23, No. 1, pp. 69-86.
34. Martin, L., Pearson, J., Furumo, K., (2007) “IS Project Management: Size, Practices and the Project Management Office,” Journal of Computer Information System, Vol. 47, No. 4, 52-60.
35. Mcdonald, D.W., (2003) “Ubiquitous Recommendation Systems,” Computer, Vol. 36, No. 10, pp. 111-112.
36. Miller, B., Albert, I., Lam S., Konstan, J., and Riedl, J., (2003) “MovieLens Unplugged: Experiences with an Occasionally Connected Recommender System,” Proceeding of IUI03, January 12-15, iami, Florida, USA, 263-266.
37. Ricci, F., and Nguyen, Q.N., (2007) “Acquiring and Revising Preferences in a Critique-Based Mobile Recommender System,” IEEE Intelligent Systems, Vol. 22, No.3, pp. 22-29.
38. Schafer, J.B., Konstan, J. and Riedl, J., (1999) “Recommender Systems in E-Commerce,” ACM Conference on Electronic Commerce (EC-99), pp. 158-166.
39. Shin, K.S. and Han, I., (1999) “Case-Based Reasoning Suuported by Genetic Algorithms for Corporate Bond Rating,” Expert systems with Application, Vol. 16, pp. 85-95.
40. Smyth, B., Keane, M., and Cunningham, P., (2001) “Hierarchical Case-Based Reasoning Integrating Case-Based and Decompositional Problem-Solving Techniques for Plant-Control Software Design”, IEEE Transactions on Knowledge and Data Engineering, Vol. 13, No.5, pp. 793-812.
41. Standish Group International, INC., 2004. Third Quarter Research Report.
42. Tesch, D., Kloppenbborg, T., Frolick, M., “IT Project Risk Factors: The Project Management Processionals Perspective,” Journal of Computer Information System, Vol. 47, No. 4, 2007, 61-69.
43. Wallace, L., Keil, M., and Rai, A., “Understanding Software Project Risk: a Cluster Analysis,” Information & Management, Vol. 42, 2004, 115-125.
44. Wang, C.S., and Chang, C.T., (2008) “Integrated genetic algorithm and goal programming for network optimization problems with multiple objective and multiple criteria”, to be published in IEEE / ACM Transactions on Networking.
45. Wang, C.S. and Tzeng, Y.R., (2007) “A Prediction Model for Policy Loan of Insurance Company”, Proceeding of The first International Workshop on Web Mining for E-commerce and E-services.
46. Yang, H.L., and Wang, C.S., (2007a) “Two Stages of Case-Based Reasoning --- Integrating Genetic Algorithm with Data Mining Mechanisms,” to be published in Expert Systems with Applications.
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53. Zeng, F., (2004) A New Approach to Integrate Computer Technology Certifications into Computer Information System Programs. Proceedings of the 2004 American Society for Engineering Education Annual Conference & Exposition.
描述 博士
國立政治大學
資訊管理研究所
93356506
97
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0093356506
資料類型 thesis
dc.contributor.advisor 楊亨利zh_TW
dc.contributor.advisor Yang, Heng Lien_US
dc.contributor.author (Authors) 王貞淑zh_TW
dc.contributor.author (Authors) Wang, Chen Shuen_US
dc.creator (作者) 王貞淑zh_TW
dc.creator (作者) Wang, Chen Shuen_US
dc.date (日期) 2008en_US
dc.date.accessioned 14-Sep-2009 09:13:35 (UTC+8)-
dc.date.available 14-Sep-2009 09:13:35 (UTC+8)-
dc.date.issued (上傳時間) 14-Sep-2009 09:13:35 (UTC+8)-
dc.identifier (Other Identifiers) G0093356506en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/31085-
dc.description (描述) 博士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理研究所zh_TW
dc.description (描述) 93356506zh_TW
dc.description (描述) 97zh_TW
dc.description.abstract (摘要) 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。		zh_TW
dc.description.abstract (摘要) 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.		en_US
dc.description.tableofcontents 誌謝 II
     摘　要 III
     ABSTRACT V
     目錄 VII
     圖目錄 IX
     表目錄 XI
     表目錄 XI
     第一章 緒論 - 1 -
      第一節 研究動機 - 1 -
      第二節 研究問題 - 3 -
      第三節 研究目的與範圍 - 9 -
     第二章 文獻探討 - 12 -
      第一節 推薦機制 - 12 -
      第二節 案例推導法 - 14 -
     第三章 研究方法與進行步驟 - 18 -
     第四章 階層式條件個案推薦決策問題 - 20 -
      第一節 階層式條件決策問題描述模型 - 20 -
      第二節 HCA決策問題權重標準化 - 23 -
     第五章 改良式個案推薦機制模型架構 - 27 -
      第一節 階層式條件之多維度推薦機制 - 27 -
      第二節 多階段個案推薦 - 39 -
      第三節　結合基因演算法求解:GCBR - 47 -
     第六章 實驗設計與實驗結果分析 - 54 -
      第一節 單階段個案推薦實驗:以旅遊規劃推為例 - 55 -
      第二節 多階段個案推薦實驗:以旅遊規劃推為例 - 62 -
     第七章　結論與研究貢獻 - 77 -
      第一節 研究貢獻 - 77 -
      第二節　實務應用 - 79 -
      第三節未來研究方向 - 85 -
     參考文獻 - 88 -		zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0093356506en_US
dc.subject (關鍵詞) 推薦機制zh_TW
dc.subject (關鍵詞) 個案推論法zh_TW
dc.subject (關鍵詞) 人工智慧技術整併zh_TW
dc.title (題名) 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zh_TW
dc.title (題名) Enhanced Case-Based Recommender Mechanism:Hierarchical Case-Retrieved Criteria and Multiple-Stage CBR Algorithmen_US
dc.type (資料類型) thesisen
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