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論文中文名稱:以案例式推薦系統提高業務銷售機會 [以論文名稱查詢館藏系統]
論文英文名稱:Improve forecast accuracy of sales opportunities Using Case Based Reasoning [以論文名稱查詢館藏系統]
英文姓名:Chien-Jung Tsai
指導教授英文名:Chen-Shu Wang
英文關鍵詞:Case Based Reasoning、Data Mining、Estimated sales opportunities
為解決銷售機會預估準確度的問題,本研究提出以案例式推薦系統(Case-Based Reasoning CBR),基於過去之歷史資料進行接單機率推薦,並輔以資料探勘之資料關聯規則進行資料關聯探勘,以探勘之結果提供給予關聯屬性之權重設定,強化推薦系統之準確度。
論文英文摘要:Estimation of Sales opportunities is the most key important performance measures of sales representative , and an important part to project the revenue for sales manager, therefore the accuracy of sales opportunities forecast directly affect the behavior patterns of sales, while the existing estimated methodologies are most like self-estimated by sales person, or define sales funnel by the sales stage, somewhat lose inaccurate on reliability.
To solve accuracy problem of sales opportunities forecasting, this study proposes a case-based recommending system (Case-Based Reasoning CBR), based on historical data to proposed a recommend chance of orders , which is supported by data mining , analyzing data relationship, exploring data, and providing weighting for relevant attribute to reinforce for all the data accuracy.
The results found that the CBR system for inexperienced or junior sales person are indeed able to provide effective reference data, and then convert existing data into knowledge, to achieve heritage purposes.
Leverage CBR systems is helping knowledge transfer by using legacy data, while currently the weightage should be judge and analysis by domain expert, and request regularly review for the fitment, therefore automatic adjust for specific characteristic can be a research in the future.
論文目次:摘要 i
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1 研究背景與動機 1
1.2研究目的 3
1.3 研究範圍與限制 3
1.4 研究架構 4
第二章 文獻探討 6
2.1 推薦系統 6
2.1.1內容推薦 6
2.1.2協同式推薦 7
2.1.3混合式推薦 7
2.1.4 推薦系統相關文獻彙整 8
2.2 案例式推薦 9
2.2.1案例式推薦流程 11
2.2.2案例式推薦相關文獻彙整 11
2.3 資料探勘 13
2.3.1資料關聯(Association) 13
2.3.2 資料分類(Classification) 13
2.3.3 推估(Estimation) 14
2.3.4資料分群(Data Clustering) 14
2.3.5循序樣式探勘(Sequential patterns mining) 14
2.3.6資料探勘相關文獻彙整 15
2.4 本研究採用方式 15
第三章 研究模型與方法 16
3.1 研究模型 16
3.2 資料整理及辨識 17
3.2.1原始資料確認及判讀 17
3.2.2確認樣本資料條件 18
3.2.3樣品特徵值說明 18
3.3 關聯分析及推薦案例擷取 20
3.3.1資料探勘: Association 20
3.3.2案例擷取方式 21
3.3.3案例擷取步驟說明 25
3.4 推薦系統架構 26
第四章 系統設計與驗證 28
4.1 資料萃取及分析 28
4.1.1資料萃取 28
4.1.2資料分析 29
4.1.3資料關聯分析 36
4.2 系統撰寫 38
4.2.1特徵值計算權重設定 38
4.2.2推薦系統撰寫 39
4.3 系統驗證 41
4.3.1可行性驗證 41
4.3.2操作人員回饋 44
第五章 結論 45
5.1 結論 45
5.2 應用上的發展 45
5.3 未來展望 46
參考文獻 48
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