臺灣博碩士論文加值系統

購屋者獲取房屋資訊的主要方式，是到房屋仲介公司的網站搜尋或是使用業者提供的APP應用程式搜尋，但網站裏成千上萬的房屋物件，通常必需花許多時間瀏覽，即使挑出來一些可能符合需求的房屋，要全部每間房子都看完也要花很長的時間，因此，如果能夠更準確更快速的找到符合需求的房屋，將可以有效的提升找房子的效率。本研究試圖改善找房子的效率，使用「意圖導向」的推薦方式來增進使用者搜尋房屋的精準度。
在房仲業者官網，本研究改善了推薦方式，並且收集使用者搜尋數據進行分析，另一方面，製作新的APP應用程式，並且透過訪談有經驗的從業人員，以瞭解此APP應用程式是否可以有效提升搜尋準確度，而得到以下結論：
一、「混合式過濾法」的推薦方式優於「內容導向過濾法」的推薦方式。
二、「意圖導向推薦機制」優於「混合式過濾法」的推薦方式。
三、「意圖導向推薦機制」得到更多的預約看屋比例。
四、「意圖導向推薦機制」所設計的各項功能，能夠更精準的推薦物件給客戶。
五、「意圖導向推薦系統」的設計能夠促進業務人員與客戶互動的有效性、能夠更快速瞭解客戶的需求，可增加業務推展工作的效用。
「意圖導向系統」的應用可以讓使用者更快速的找到房屋物件，提升整體的交易效率，不只房屋仲介業者可以擴大應用範圍，各個產業也值得推廣開發更多的應用。

The main way that homebuyers obtain housing information, is realty agency''s Web site or using APP of agents provided search. Thousands of houses objects in the website usually take a lot of time browsing. Even homebuyers select the houses they might need, they have to spend a lot of time to look these houses. If there are more accurate and more quickly meet the demand for houses, will be able to improve the efficiency of looking for houses. This study attempts to improve the efficiency of looking for houses, use the "intentioned based" is the recommended way to enhance a user''s search houses accuracy.
Realty agency in the official website, the study improves recommended way and collect users'' search data for analysis. On the other hand, to create a new APP and to interview with experienced practitioners explore this APP whether can effectively improve searching accuracy. This study bring the following conclusions.
First, the recommended way of "Hybrid Filtering" is better than the recommended way of "Content-based Filtering".
Second, the "Intentioned based recommendation mechanism" is better than the recommended way of "Hybrid Filtering".
Third, the "Intentioned based recommendation mechanism" get more booking looking house ratio.
Fourth, "Intentioned based recommendation system" designed function will be able to more accurate recommendation object to the customer.
Fifth, the "Intentioned based recommendation system" is designed to promote the salesman to interact with customers, more quickly understand customers needs and increase the effectiveness of sales work.
Application of "Intentioned based system" can allow users to find houses objects more quickly to enhance the overall efficiency of the transaction. Not only can realty agency expand the range of applications, but it is also worth developing more applications in various industries.

目 錄
摘要……………………………………………………………………i
Abstract………………………………………………………………ii
目錄……………………………………………………………………iii
圖目錄…………………………………………………………………iv
表目錄……………………………………………………… …………v
第一章、 緒論…………………………………………………………1
第一節 研究背景……………………………………………………1
第二節 研究動機和目的……………………………………………2
第三節 研究流程……………………………………………………4
第二章、 文獻探討……………………………………………………5
第一節 推薦系統……………………………………………………6
第二節 計畫行為理論……………………………………………..11
第三章、 研究方法…………………………………………………..15
第一節 意圖導向推薦系統………………………………………..15
第二節 意圖導向推薦系統在官網上及APP上的應用…………...21
第三節 分析方法…………………………………………………..25
第四章、 系統分析與設計…………………………………………..28
第一節 系統需求與分析…………………………………………..28
第二節 系統設計與實作…………………………………………..32
第五章、 研究結果…………………………………………………..42
第一節 意圖導向推薦系統在官綱的使用結果…………………..42
第二節 意圖導向推薦系統在APP上的使用結果………………...45
第三節 意圖導向推薦系統對房仲業務使用的有效性…………..51
第六章、 結論與建議………………………………………………..53
第一節 研究結論…………………………………………………..53
第二節 未來建議…………………………………………………..54
第三節 研究限制…………………………………………………..55

參考文獻……………………………………………………………….57

一、中文部分
1. 李墾典(2004)， 消費者使用不動產銷售網站認知與態度之研究， 逢甲大學碩士論文。
2. 李筱瑜、李豐良(2000)， 電子商務對房仲業影響之實證研究，科技與管理學術研討會論文集，191-196。
3. 吳志宏(2004)，以隱性回饋為基礎的自動化推薦機制，朝陽科技大學資訊管理研究所論文
4.彭建文、康尚德(2001)，網際網路對不動產仲介業經營之影響分析，都市與計劃，28（2）：171-186。
5. 黃茗韋(2009)，以計畫行為理論探討代言人對消費者的購買意願之研究，南華大學碩士論文。
6. 黃維良(2007)，具有“隨時間變異的顧客購買興趣＂預測能力之多階層協同式推薦系統，國立高雄第一科技大學資訊管理研究所碩士論文。
7. 黃名義、許乃文(2012)，電子商務應用對房仲業績效之影響，國立屏東商業技術學院學報14 2012.08，頁97-123。
8. 陳世泓(2009)，適性地與適性化的智慧型行動裝置租屋推薦系統，中華大學碩士論文。
9. 羅健銘(1991)，協同過濾於網站推薦之研究，台北科技大學商業自動化與管理研究所碩士論文。
10. 葉旭榮(1997)，志工參與行為意向模式的建構及其在志工人力資源招募的應用—以老人福利機構志工招募為例，國立中山大學碩士論文。

二、英文部分
1. Adomavicius , G., & Tuzhilin, A., (2005).Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions.1041-4347/05 IEEE.
2. Ajzen, I.（1985）, From Intentions to Actins: A theory of Planned Behavior, in J. Kuhl and J. Bechmann（Eds.）, Action-Control: From Cognition to Behavior, pp.11-39.
3. Ajzen, I.,（1989）, Attitude Structure and Behavior , Lawrence Erlbaum Associates, Hillsdale, NJ, pp.241-274.
4. Ajzen, I.（1991）, The theory of planned behavior. Organizational Behavior and Human Decision Processes, Vol.50, No.2, pp.179-211.
5. Akinyele, http://www.csee.umbc.edu/~jklabrou/courses/fall1999_691f/week10 /john_akinyele_recommender_systems/491f_files/v3_document.htm, 2002/3/1.
6. Armitage, C. J., & Conner, M.（2001）, Efficacy of the theory of planned behaviour: A meta-analytic review. British Journal of Social Psychology, Vol.40, No.4, pp.471–499.
7. Badrul, S., George, K., Joseph, K., and John, R., (2000) “Analysis of Recommendation Algorithm for E-Commerce,” Communication of ACM, 2000.
8. Balabanovic, M. and Shoham, Y., (1997), “Fab: Content-based Collaborative Recommendation.” Communications of the ACM, 40(3), pp.66-72.
9. Benjamin J. D., P. Chinloy, G. D. Jud & D. T. Winkler（2005）, “Technology and Real Estate Brokerage Firm Financial Performance,” Journal of Real Estate Research. 27（4）: 409-426.
10. Bobadilla, J., Ortega, F., Hernando, A. and Gutiérrez, A. (2013), “Recommender systems survey”, Knowledge-Based Systems, Vol. 46, pp. 109-132.
11. Burke,R.(2002). Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction. November 2002, Volume 12, Issue 4, pp 331-370.
12. Davis, F. D.（1989）, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Quarterly. 13: 319-340.
13. Dhillon, N., “Achieving Effective Personalization and Customization Using Collaborative Filtering,” http:// home1.gte.net/dhillos/cf., Oct. 1995.
14. Fishbein, M. (1967). Attitude and the prediction of behavior. In Martin Fishbein (Eds.), Readings in attitude theory and measurement, 477-492. New York: Wiley.
15. Fishbein, M., & Ajzen, I.（1975）, Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Journal of Leisure Research, Vol.27, No.1, pp.61-84.
16. Fishbein, M. (1980). A theory of reasoned action: Some applications and implications. In Howe, H., & Page, M. (Eds.), Nebraska Symposium on Motivation, 27, 65-116. Lincoln, NE: University of Nebraska Press.
17. Goldberg, D., Nichols, D., Oki B.M. and Terry, D., (1992), “Using collaborative filtering to weave an information tapestry.” Communications of the ACM, 35(12), pp.61-70.
18. Gwin, C. R.（2004）, “International Comparisons of Real Estate E-Information on the Internet,” Journal of Real Estate Research. 26（1）: 1-22.
19. Hagger, M. S., Chatzisarantis, N. L. D., & Biddle, S. J. H.（2002）, A meta-analytic review of the theories of reasoned action and planned behavior in physical activity: Predictive validity and the contribution of additional variables. Journal of Sport & Exercise Psychology, Vol.24, No.1, pp.3-32.
20. Haubl ,G.,&Murray,K.B. “Beyond Personalization 2005 : A workshop on the Next Stage of Recommender System Research,” International Conference on Intelligent User Interfaces(IUI), 2005, San Diego, California.
21. Huang, Z., Chen, H., & Zeng, D. (2004). Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inf. Syst., 22(1), 116-142.
22. Johathan L.,Herlocker, J., Konstan, J., and Riedl, J. “Explaining Collaborative Filtering Recommendations,” ACM 2000 Conference on Computer Supported Cooperative Work , Dec. 2000.
23. Kahle, Lynn R. & Homer, Pamela M.（1985）, Physical Attractiveness of the Celebrity Endorser: A Social Adaptation Perspective, Journal of Consumer Research, Vol.11, pp.954-961.
24. Kim, B.D. and Kim, S.O., (2001), “A new recommender system to combine content-based and collaborative filtering systems.” The Journal of Database Marketing, 8(3), pp.244-252.
25. Krulwich, B. and Burkey, C., (1996), “Learning user information interests through extraction of semantically significant phrases.” In proceedings of the AAAI Spring symposium on Machine Learning in Information Access, Stanford, CA.
26. Krulwich, B., & Burkey, C. (1997). The InfoFinder agent: learning user interests through heuristicphrase extraction. Expert, IEEE [see also IEEE Intelligent Systems and Their Applications], 12(5), 22-27.
27. Lafferty, Barbara A. & R. E. Goldsmith（1999）, Corporate credibility’s role in consumer’s attitudes and purchase intentions when a high versus a low credibility endorser is used in the ad. Journal of business Research, Vol.44, No.2, pp.109-116.
28. Lang, K., (1995), “Newsweeder: learning to filter netnews.” In proceedings of the Machine Learning conference, pp.331-339.
29. Lee, C. & Green, R. T. (1991). Cross-cultural examination of the fishbein behavioral intentions model. Journal of International Business Studies, 289-305.
30. Massa, P., & Avesani, P. (2004). Trust-aware collaborative filtering for recommender systems. Paper presented at the Proceedings of International Conference on Cooperative Information Systems, Agia Napa, Cyprus.
31. Meteren, R.V. and Someren, M.V., (2000), “Using content-based filtering for recommendation.” In proceedings of ECML Workshop: Machine Learning in New Information Age, pp.47-56.
32. Mock, K.J. and Vemuri, V.R., (1997), “Information filtering via hill climbing, wordnet, and index patterns.” Information Processing & Management, 33(5), pp.633-644.
33. Muhanna, W. A.（2000）, “E-Commerce in the Real Estate Brokerage Industry,” Journal of Real Estate Practice and Education. 3（1）: 1-16.
34. Papagelis, M., Plexousakis, D., & Kutsuras, T. (2005). Alleviating the sparsity problem of collaborative filtering using trust inferences. Paper presented at the Proceedings of the 3rd International Conference on Trust Management. LNCS. Springer-Verlag, Rocquencourt, France.
35. Ricci, F., (2002) “Travel Recommender System,” IEEE Intelligent System, pp.55-57
36. Resnick, P., (1997) “Recommender Systems,” Communications of the ACM, 40(3), pp. 56-58.
37. Sarwar, B., Konstan, J., Borchers, A., Herlocker, J., Miller, B., and Riedl, J.,(1998), “Using Filtering Agents to Improve Prediction Quality in the Research Collaborative Filtering System,” Proceedings of the 1998 Conference on Computer Supported Cooperative Work, Nov.
38. Schafer, J. B., Konstan, J., & Riedi, J., (1999). Recommender systems in e-commerce. Paper presented at the Proceedings of the 1st ACM conference on Electronic commerce.
39. Schafer, J.B., Konstan J.A. and Riedl, J., (2001), “E-commerce recommendation applications.” Data Mining and Knowledge Discovery, (5:1), pp.115-153.
40. Schiffman, L. G. & Kanuk, L. L.（2000）, Consumer Behavior, 7th ed., Wisconsin Prentice Hall International, Inc.
41. Shardanand, U. and Maes, P., (1995), “Social snformation filtering: algorithms for automating ‘Word of Mouth’.” In Proceedings of The Conference on Human Factors in Computing Systems, pp.210-217.
42. Till, B. D. & Busler, M.（2000）, The match-up hypothesis: Physical attractiveness, expertise, and the role of fit on brand attitude, purchase intent and brand beliefs, Journal of advertising, Vol.29, No.3, pp.1-13.
43. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D.（2003）, User acceptance of information technology: Toward a unified view. MIS Quarterly, Vol.27, No.3, pp.425–478.