隨著網路技術的發展，Web 2.0的網路服務已成主流，民眾開始將生活瑣事記載於個人Blog以及在各社群網站分享自身的經驗，網路逐漸形成一種商品口碑傳播平台。民眾利用網路搜尋消費資訊，藉由他人的消費經驗分享，來決定消費的商家，但資訊量隨著時間愈來愈多，已造成資訊超載現象，民眾在面對眾多的選項上，花費過多的時間與精神在搜尋所需要的資訊。
因此，本研究設計一個商家推薦系統的服務平台，首先以台中市餐廳為例，當使用者需要選擇餐廳時，藉由使用者提供的條件資訊，利用案例式推理來篩選使用者當下條件選擇的結果，搭配內容式推薦演算法來輔助使用者查詢條件不足的情形，系統主動推薦餐廳目錄給使用者，提供個人化推薦的服務，並藉由使用者的回饋逐漸擴增案例庫，和改善系統的推薦準確率。本研究將建置一以Web 2.0為基礎之平台以應用於商務應用。
本研究以實驗測試的方式來評估系統可用性，於使用者測試系統後進行問卷調查，以了解使用者是否滿意系統推薦結果，經由統計分析得知本系統具有可用性。本研究成果可供其他店家推薦系統之參考。

With Internet technology development, Web 2.0 for web service has become mainstream, people began to record their daily life in their Blog and various community sites to share their life experiences. Therefore, Web service becomes a communication platform gradually. However, people can use the Web service system to search for information of consumption and experience sharing by the consumption of others to determine how to consume. But, the amount of information over time more and more, has resulted in information overload. Therefore, people always acquire many options, spend too much time and efforts in their searching information task.
This study designs an e-commerce recommendation system with web service function, a case study of restaurants in Taichung city. When users need to acquire consuming information of restaurants, they can apply this recommendation system for acquiring the information they need. The system automatically recommend restaurant information list to users, offers personalized recommendation service, and expansion of case base gradually by the user''s feedback, and improve the accuracy of recommendation system. However, this study concludes with ideas for Web 2.0 applications of recommendation system to e-commerce.
In the study, the way we use experimental testing to assess system availability, in a user survey conducted after the test system to see whether the user is satisfied with the system recommended a result, by statistical analysis that the system has available. The research results available to other business recommendation systems of reference.

摘要 i
ABSTRACT iii
圖目錄 v
表目錄 vi
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 3
1.4 研究流程 4
1.5 研究範圍 5
第二章 文獻探討 6
2.1案例式推理 6
2.2推薦系統 8
2.3內容式過濾(content-based filtering) 9
2.4 Web 2.0 14
第三章 系統建置與方法 20
3.1系統建置環境 20
3.2系統架構 22
3.3案例式模組 23
3.3.1 推薦方法 23
3.3.2 系統畫面 26
3.4內容式推薦模組 29
3.4.1 餐廳 profile 29
3.4.2 使用者 profile 29
3.4.3 系統畫面 33
3.4.4 推薦流程 36
3.5 Web 2.0服務模組 38
第四章 可用性評估方法 42
4.1受測人員 42
4.2 實驗步驟與設備 42
4.3問卷設計 44
4.4操作定義 44
4.5資料分析方法 45
第五章 統計分析 46
5.1基本資料分析 46
5.2任務需求分析 47
5.3因素分析與信度 49
5.4可用性反應統計分析 53
第六章 討論 57
6.1 使用者分析與需求 57
6.2 系統可用性 58
第七章 結論與未來研究方向 59
參考文獻 61
附錄A－任務情境 65
附錄B－問卷設計 67

英文部分
Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI communications, 7(1), 39-59.
Allen, B. (1994). Case-based reasoning: business applications. Communications of the ACM, 37(3), 40-42.
Anandanpillai, T., & Barta, T. A. (1999). A case-based reasoning system for housing discrimination law. Expert Systems with Applications, 16(3), 315-324.
Bichindaritz, I., & Marling, C. (2006). Case-based reasoning in the health sciences: What''s next? Artificial Intelligence in Medicine, 36(2), 127-135.
Bradburn, C., & Zeleznikow, J. (1994). The application of case-based reasoning to the tasks of health care planning. Topics in Case-Based Reasoning, 365-378.
Chang, C. L. (2005). Using case-based reasoning to diagnostic screening of children with developmental delay. Expert Systems with Applications, 28(2), 237-247.
Chou, P. H., Li, P. H., Chen, K. K., & Wu, M. J. (2010). Integrating web mining and neural network for personalized e-commerce automatic service. Expert Systems with Applications, 37(4), 2898-2910.
Engel, J. F., Kollat, D. T., & Blackwell, R. D. (1982). Consumer Behavior (4th ed.). New York: Holt, Rinehart and Winston.
Jung, C., Han, I., & Suh, B. (1999). Risk analysis for electronic commerce using case-based reasoning. Intelligent Systems in Accounting, Finance & Management, 8(1), 61-73.
Kolonder, J. (1993). Case-based reasoning. San Mateo: Morgan Kufman.
Kumar, K. A., Singh, Y., & Sanyal, S. (2009). Hybrid approach using case-based reasoning and rule-based reasoning for domain independent clinical decision support in ICU. Expert Systems with Applications, 36(1), 65-71.
Lang, K. (1995). Newsweeder: Learning to filter netnews. Paper presented at the In Proceedings of the Twelfth International Conference on Machine Learning, San Francisco, CA. Morgan Kaufman, 331-339
Lee, M. K. O., Shi, N., Cheung, C. M. K., Lim, K. H., & Sia, C. L. (2011). Consumer''s decision to shop online: The moderating role of positive informational social influence. Information & Management, 48(6), 185-191.
Liang, T.-P., & Lai, H.-J. (2002). Effect of store design on consumer purchases: an empirical study of on-line bookstores. Information & Management, 39(6), 431-444.
Malone, T., Grant, K., Turbak, F., Brobst, S., & Cohen, M. (1987). Intelligent information-sharing systems. Communications of the ACM, 30(5), 390-402.
Paek, Y. K., Seo, J., & Kim, G. C. (1996). An expert system with case-based reasoning for database schema design. Decision Support Systems, 18(1), 83-95.
Park, Y. J., & Chang, K. N. (2009). Individual and group behavior-based customer profile model for personalized product recommendation. Expert Systems with Applications, 36(2, Part 1), 1932-1939.
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). GroupLens: an open architecture for collaborative filtering of netnews. Paper presented at the 1994 ACM conference on Computer supported cooperative work, 175-186.
Schafer, J., Konstan, J., & Riedi, J. (1999). Recommender systems in e-commerce. Paper presented at the 1st ACM conference on Electronic commerce(EC-99), Denver, Colorado, USA, 158-166.
Schmitt, S., & Bergmann, R. (1999). Applying case-based reasoning technology for product selection and customization in electronic commerce environments. Paper presented at the 12th International Bled Electronic Commerce Conference, Bled, Slovenia, 273.
Shang, S. S. C., Li, E. Y., Wu, Y. L., & Hou, O. C. L. (2011). Understanding Web 2.0 service models: A knowledge-creating perspective. Information & Management, 48(4-5), 178-184.
Suh, K. S. (1999). Impact of communication medium on task performance and satisfaction: an examination of media-richness theory. Information & Management, 35(5), 295-312.

Thomasson, B. J., Ratcliffe, M. B., & Thomas, L. A. (2006). Improving the tutoring of software design using case-based reasoning. Advanced Engineering Informatics, 20(4), 351-362.
Wang, F. H., & Shao, H. M. (2004). Effective personalized recommendation based on time-framed navigation clustering and association mining. Expert Systems with Applications, 27(3), 365-377.
Weng, S. S., & Liu, M. J. (2004). Feature-based recommendations for one-to-one marketing. Expert Systems with Applications, 26(4), 493-508.
Yuan, F. C., & Chiu, C. (2009). A hierarchical design of case-based reasoning in the balanced scorecard application. Expert Systems with Applications, 36(1), 333-342.
中文部分
李察古森. (2005). 網民經濟學 運用Web2.0群眾智慧搶得商機.
劉籹君, 黃興進, & 廖則竣. (2010). 決策支援系統使用績效之實證研究：結合任務-科技適配與資訊系統成功模式. 電子商務學報, 12卷(3期), 頁407-430.
網址部分
O''Reilly, T. (2005, 09/30). What Is Web 2.0. from http://tim.oreilly.com/news/2005/09/30/what-is-web-20.html