電子商務在網路上的茁壯，讓消費者可以在網路上方便消費，但面對琳瑯滿目的商品，消費者不易於選擇符合他們所需求的產品。而推薦系統的發展便是為了方便消費者在龐大的可能選擇之中找尋符合他們的需求。
推薦系統發展至今應用廣泛，從電子商務的服務到推薦書籍、餐廳、電玩、旅行路線等等現實生活上的需求皆有涵蓋，但是推薦穿著搭配這一環是較少人觸及的。
在現實生活裡，人們想了解不同場合所適合的穿著，自己的身材該如何穿，怎樣的顏色搭配比較好，這都是使用者在搭配穿著上所遭遇到困難，人們因此會去詢問別人意見或是參考時尚雜誌，從這樣的行為下人們所找尋的資訊都是具體的衣服屬性上的對應，例如顏色、廠牌、材質、大小和種類。
根據這樣的想法，本論文提出一個情境式個人化穿著推薦系統，系統透過收集使用者所描述的具體之衣服屬性、使用者的偏好和色彩學意象上的分析來建立出使用者的個人穿著知識，幫助使用者快速找尋針對特定場合下適合且喜愛的穿著，從實驗的結果表示本系統在對人們的穿著搭配上是有實質的幫助。

Electronic commerce in Internet is thriving. Even though consumers can effortlessly consume from the Internet, they still have troubles in finding products and commodities that will meet their needs and desires. The development of recommendation Systems is absolutely helpful for consumers to locate products that will meet their needs and desires in the messes of commodities. Recommendation Systems have been widely used for services of electronic commerce on any needs of real life, including books, restaurants, electronic games, tourist paths and so on, nevertheless, they are rarely used in fashion and outfit of clothes.
In real life, people may have difficulties in finding suitable outfits for different occasions, their own stature and favorite color combinations. They then try to solicit others or refer to fashion magazines for suggestions. In these ways, people gather information of specific clothing attributes, such as colors, brands, material, sizes, and types.
Accordingly, this thesis presents a contextual personalized wearing recommendation system. It creates a user’s personal wearing knowledge and help the user conveniently find suitable and favorite outfits for particular occasions through gathering users’ descriptions of specific clothing attributes, users’ preference and color images analysis. The results of experiments show the potential usability of this system in helping people in dressing.

[1] B. Balabnovic and Y. Shoham, “Fab: Content-based, Collaborative Recommendation,” Proceedings of 14th Conference on Uncertainty in Artificial Intelligence, 1998.
[2] B.N. Miller, I. Albert, S.K. Lam, J.A. Konstan, and J. Riedl, "MovieLens Unplugged: Experiences with an Occasionally Contented Recommender System." Proc. Int'l Conf. Intelligent User Interfaces, 2003.
[3] C.C. Peddy and D. Armentrout, “Building Solutions with Microsoft Commerce Server 2002.” Microsoft Press, 2003.
[4] D. Billsus and M. Pazzani, ”User Modeling for Adaptive News Access,” User Modeling and User-Adapted Interaction, vol. 10, nos. 2-3, pp. 147-180, 2000.
[5] D. Billsus, C.A. Brunk, C. Evans, B. Gladish, and M. Pazzani, “Adaptive Interfaces for Ubiquitous Web Access.” Comm, ACM, vol.45, no. 5, pp.34-38,2002.
[6] D. Goldberg, D. Nichols, B.M. Oki, and D. Terry, “Using Collaborative Filtering to Weave an Information Tapestry,” Comm. ACM, vol. 35, no. 12, pp. 67-70,1991.
[7] F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology.” MIS Quarterly, 13(3), 319-340, 1989.
[8] G. Adomavicius and A. Tuzhilin, “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions,” IEEE Transactions on Knowledge and Data Engineering, vol.17(6), pp.734-749, 2005.
[9] G. Linden, B. Smith, and J. York, “Amzaon.com Recommendation: Item-to-Item Collaborative filtering.” IEEE Internet Computing, Jan./Feb. 2003
[10] G. Salton, “Automatic Text Processing.” Addison-Wesley, 1989.
[11] J.B. Schafer, J. Konstan and J. Riedl, “E-commerce recommendation applications,” Data Mining and Knowledge Discovery, vol.5(1-2), pp. 115-153,2001.
[12] J.J. Rocchio, “Relevance Feedback in Information Retrieval,” SMART Retrieval System- Experiments in Automatic Document Processing, G. Salton, ed., chapter 14, Prentice Hall, 1971.
[13] J.L. Herlocker, J.A. Konstan and L.G. Terveen, “Evaluating collaborative filtering recommender system,” ACM Transactions on Information Systems, vol.22(1), pp. 5-53,2004.
[14] J.S. Breese, D. Heckerman and C. Kadie, “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, 1998.
[15] M. Pazzani and D. Billsus, “Learning and Revising User Profiles: The Identification of Interesting Web Sites,” Machine Learning, vol. 27, pp. 313-331, 1997.
[16] M. Pazzani, “A Framework for Collaborative, Content-Based, and Demographic Filtering,” Artificial Intelligence Rev., pp. 393-408, Dec. 1999.
[17] N. Belkin and B.Croft, “Information Filtering and Information Retrieval.” Comm. ACM, vol. 35, no. 12, pp. 29-37, 1992.
[18] P. Resnick and H.R. Varian, “Recommender Systems,” Communications of the ACM, vol.40(3), pp. 56-58, 1997.
[19] P. Resnick, N. Iakovou, M.Sushak, P. Bergstrom, and J. Riedl, “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” Proc. 1994 Computer Supported Cooperative Work Conf., 1994.
[20] P. Sweetser and P. Wyeth, “GameFlow: a model for evaluating player enjoyment in games,” ACM Computers in Entertainment(CIE), vol.3(3),2005.
[21] R. Baeza-Yates and B. Ribeiro-Neto, “Modern Information Retrieval.” Addison-Wesley, 1999.
[22] R. Burke, “Hybrid Recommender systems: survey and experiment,” User Model. User Adapt. Inter., vol.12, pp. 331-370,2002.
[23] R. Burke, ”Knowledge-Based Recommender Systems,” Encyclopedia of Library and Information System, A. Kent, ed., vol. 69, Supplement 32, Marcel Dekker, 2000.
[24] S.E. Middleton, N.R. Shadbolt, and D.C. de Roure, “Ontological User Profiling in Recommender Systems,” ACM Trans. Information Systems, vol. 22, no. 1, pp. 54-88, 2004.
[25] T.R. Gruber, “A translation approach to portable ontologies,” Knowledge Acquisition, vol. 5, No. 2, pp. 199-220, 1993
[26] U. Shardanand and P. Maes, “Social Information Filtering:Algorithms for Automating ‘Word of Mouth’,” Proceedings of ACM CHI’95 Conference on Human Factors in Computing Systems, vol.1, pp. 210-217,1995.
[27] 歐秀明，賴來洋，“實用色彩學”， 雄獅圖書公司， 八十八年。
[28] Youngjin.com著，”給設計師的專業配色典 - The Color for Designer.”，博碩文化，2009。
[29] http://asia.playstation.com/tw/promo/mystylist/mystylist.html
[30] http://ilikemystyle.net/
[31] http://zh.wikipedia.org/wiki/Web_2.0
[32] http://www.diija.com.tw
[33] http://www.iricolor.com/main.asp