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研究生:陳昱賓
研究生(外文):Yu-Pin Chen
論文名稱:運用基因演算法於個人化推薦系統-以電話行銷為例
論文名稱(外文):Using Genetic Algorithm for Personalized Recommendation-Using Telemarketing As An Example
指導教授:黃謙順黃謙順引用關係
指導教授(外文):Chein-Shung Hwang
學位類別:碩士
校院名稱:中國文化大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:74
中文關鍵詞:推薦系統基因演算法資料探勘協同過濾演算法
外文關鍵詞:recommender systemgenetic algorithmdata miningcollaborative filtering approach
相關次數:
  • 被引用被引用:0
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  • 收藏至我的研究室書目清單書目收藏:5
近年來資訊科技的快速發展促使電話行銷產業在台灣變得更加活絡,各產業積極的投入使得此行銷通路蓬勃發展。不論B2B或B2C的企業,只要擁有適合電話行銷通路的產品與服務,加上資料庫的應用,就可以根據企業本身具有的通路優勢,達到執行電話行銷業務的推動。但是身為電話行銷人員,往往因為長期做電話行銷或銷售活動,迫使自己面對一連串的壓力和挫折,使得自己陷入空前的絕望和沮喪中。對企業而言,將造成「人力」、「時間」和「成本」的浪費。因此,如何幫助電話行銷人員有效地找尋到更多潛在顧客,減少被拒絕的機會與壓力,是需要一套良好的輔助工具來配合。而個人化推薦系統能夠利用過去電話訪談、成交紀錄建立預測模型,協助電話行銷人員找出較有購買意願的潛在顧客,讓行銷人員不必再亂槍打鳥,而提昇電話銷售成交率。同時也可以節省顧客寶貴的搜尋時間,提高顧客在此企業購買商品的忠誠度,更可以增加企業的收益。本研究主要描述個人化推薦系統如何有效的運用基因演算法來學習顧客的喜好並提供合適建議給顧客。
Nowadays, with the fast speed of information technology development, the tele-marketing becomes more active in Taiwan. Hence, most companies are seeking how to reduce their costs by adopt telemarketing. No matter the enterprises of B2B or B2C,they can take advantage of their own telemarketing combining with their products, ser-vice, and database to increase the successful rate of telemarketing. As telemarketing staffs, they often do more telemarketing or marketing activities for a long time, force themselves to face a serious of pressure and setback, make themselves in deep despair. As to enterprise, it will cause the waste of 'manpower', 'time' and 'cost'. Therefore, how to help the telemarketing staffs to look for more potential customers effectively and re-duce the reject and pressure need a set of good handling tools to cooperate. Recom-mender system can build a prediction model by utilizing telephone interview, and sell-ing records. It can help the enterprise to find the prospective customers and improve the success rate of telemarketing, the system can also increase the customers’ loyalty and enterprises’ revenue. This paper describes a new recommender system, which employs a genetic algorithm to learn personal preferences of users and provide tailored sugges-tions.
中文摘要 ...................... iii
英文摘要 ...................... iv
誌謝辭  ...................... v
內容目錄 ...................... vi
表目錄  ...................... viii
圖目錄  ...................... x
第一章  緒論.................... 1
  第一節  研究背景................ 1
  第二節  研究動機................ 1
第三節 研究目的................ 2
第四節 研究對象................ 3
第五節  論文架構與研究流程........... 3
第二章  文獻探討.................. 6
  第一節  推薦系統................ 6
  第二節  協同過濾方法.............. 12
  第三節  基因演算法............... 18
第三章 研究方法.................. 30
第一節 研究架構................ 30
第二節 資料預處理............... 33
第三節 輪廓產生模組.............. 36
第四節 鄰居選擇模組.............. 39
第五節 推薦模組................ 43
第四章 實驗與結果分析............... 45
第一節 資料預處理............... 45
第二節 實驗環境與工具............. 46
第三節 評估因子................ 46
第四節 實驗設計................ 47
第五節 實驗結果................ 57
第五章 結論與建議................. 58
參考文獻 ...................... 60
附錄A 突變率與交配率之實驗結果.......... 66
一、中文部份
江政達(2001),提供個人化服務為銷售網站提高集客力之要件[線上資料],來源:http://www.find.org.tw/0105/news/0105_news
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二、英文部份
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Chee, S. H. S., Han, J., & Wang, K. (2001). RecTree: An efficient col-laborative filtering method. Proceedings of International Conference on Data Warehouse and Knowledge Discovery, 141-151.
Cooley, R., Mobasher, B., & Srivastava, J. (1997). Web mining: Information and pattern discovery on the World Wide Web. In IEEE Computer Society (Ed.), Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence. (pp. 558-567), Newport Beach.
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Hughes, A. M. (1994). Strategic Database Marketing. Chicago:Probus Publishing.
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Nakamura, A., & Abe, N. (1998). Collaborative filtering using weighted majority prediction algorithms. In ICML (Ed.), Proceedings of the Fifteenth International Conference on Machine Learning (pp. 395-403). San Francisco: Morgan Kaufmann Publishers Inc.
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Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000). Analysis of Recommendation Algorithms for E-Commerce. Proceedings of the ACM E-Commerce 2000 Conference, 158-167.
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In ACM (Ed.), Proceedings of the 10th International WWW Conference (pp. 285-295). Hong Kong: ACM Press.
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Schafer, J. B., Konstan, J., & Riedl, J. (1999). Recommender system in e-commerce. Proceedings of the first ACM conference on Electronic Commerce, 158-166.
Shardanand, U., & Maes, P. (1995). Social Information Filtering: algorithms for automating ‘Word of Mouth’. Proceedings of the Conference on Human Factors in Computing Systems, 210-217.
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Weng, Sung-Shun, & Liu, Mei-Ju. (2004). Feature-based recommendations for one-to-one marketing. Expert Systems with Applications, 26(4), 493-508.
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