Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/70977
|
| Title: | 電子賀卡自助式設計系統－以互動演化式計算為基礎
The development of self-design system for greeting cards based on interactive evolutionary computing |
| Authors: | 楊筱芳
Yang, Hsiao Fang |
| Contributors: | 楊亨利
Yang, Heng Li
楊筱芳
Yang, Hsiao Fang |
| Keywords: | 互動演化式計算
自助
賀卡
創意設計
interactive evolutionary computing
do-it-yourself
greeting card
creative design |
| Date: | 2013 |
| Issue Date: | 2014-11-03 10:08:42 (UTC+8) |
| Abstract: | 企業製程走向模組化，資訊技術持續進步，市場競爭激烈，產品生命週期縮短，市面上充斥著各式各樣的產品（資訊過載）。行銷3.0的年代又稱之為參與者的年代，消費者開始要求互動與共創價值（創意），以用戶為中心的產品設計逐漸受到重視，特別是數位設計。與系統互動的過程中，可能會面臨人們的需求改變（需求不明確）與需求無法明確描述（資料稀少）的問題。因此，本論文以互動演化式計算為核心，以自助式概念提出一個賀卡設計系統（名為SDGCS），用以解決資訊過載、資料稀少與需求不明確的創意設計問題。
在資料處理階段，SDGCS提出新的影像處理方式，結合質性與量化的資料，讓影像能夠進行更精準的比對。在進入系統的操作階段，SDGCS以專家設計的影像布置，讓非專家的使用者能輕鬆設計。在互動階段，SDGCS提供使用者多種自助模式（如影像拖曳、影像多種幅度的改變），讓使用者在有了明確設計方向後，可以自己主導與更快完成設計。
為確保兩組受測者的同質性，本論文以問卷評測進行分組，然後才進入實驗。本論文比較傳統互動演化式計算的系統（名為GCS）與SDGCS受測者的系統操作內容與系統使用的認同度（問卷），實驗結果指出：一、SDGCS的使用者比GCS的使用者更投入在賀卡內容的設計，二、不論是SDGCS或是GCS，專家提供的賀卡布置讓使用者能夠很快就完成賀卡封面設計，三、SDGCS的使用者可以在短的搜尋次數裡找到合用的影像來進行賀卡封面設計，四、GCS或SDGCS都能取得使用者的認同，但是GCS的使用者渴望使用賀卡封面內物件的變化（也就是SDGCS所提供的功能）。五、多數受測者滿足SDGCS所提供的自助功能，少數受測者追求更精緻的自助功能。
本論文以自助概念嵌入互動演化式計算的系統解決資訊過載、資料稀少與需求不明確的創意設計問題，但是數位產品的設計不只是只有影像組合，未來的研究應該可以更深入的探討文字的意涵與風格等問題。
Business manufacturing processes are moving towards modularity. Because of continuing advances in information technology and market competition, there is a tendency of shortened product life cycles, and a wide variety of products can be seen in the market (i.e. information overload). Marketing 3.0 is also known as the age of the participant`s age. Consumers started to request interaction with designers to create the value (creativity) of a product. User-centered product designs have attracted more and more attention, especially digital designs. In the course of interacting with the system, designers may face some issues, such as changes in people`s demands (i.e. unclear demands) and insufficient descriptions of people’s demands (i.e. data scarcity). Therefore, in order to solve the problems of information overload, creativity, and data scarcity, the thesis research was done to offer a self-design greeting card system (SDGCS) by the use of interactive evolutionary computing.
In the stage of processing the data, the SDGCS provides a new way of image processing that combines qualitative and quantitative data. It allows images to be more accurately found. In the operational stage, the SDGCS provides professional design layouts, and make it easy for non-professional users to design. In the interaction stage, the SDGCS offers users a variety of self-design modes, such as movement of images, changes in levels of image. It allows users to have a better idea of designing a card and can complete their designs in an autonomous way more quickly.
Before carrying out the experiment, in order to ensure the homogeneity of the two groups of participants, participants were grouped based on questionnaire results. Then, the researcher moved on to do the experiment. The researcher used a questionnaire to compared participants’ operation and experiences of using traditional interactive evolutionary computing system (GCS) and SDGCS. Research results indicate that, first, the participants of the SDGCS group were more engaged in than the participants of the GCS group were. Second, both the SDGCS and the GCS groups can quickly complete a greeting card cover design, using the professional greeting card layout provided. Third, participants of the SDGCS can find suitable images for greeting card cover design in only a limited times of search. Fourth, Both the GCS or the SDGCS are acceptable to participants of the research. However, the GCS groups claimed to prefer to have various designs in card image, which is one of the featured functions provided by the SDGCS). Fifth, a lot of people satisfy functions provided by the SDGCS; a few peoples pursue elaborate self-design functions.
In this paper, the researcher used self-design based interactive evolutionary computing to solve the problems of information overload, creativity, and data scarcity in digital designing. However, the design of digital products is more than the integration of images. Future research can be conducted to explore what messages the texts of a greeting card intend to convey, the style of a greeting card, and so on. |
| Reference: | 1. 李銘龍（2010）。色彩原理。台北市：龍騰文化事業股份有限公司。
2. 洪飛恭（2011）。客製化模組產品推薦服務系統建立之研究（博士論文）。取自http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dnclcdr&s=id=%22099NHU05457001%22.&searchmode=basic。
3. 許芳誠（2000）。智慧型多準則決策支援研究：以交談式遺傳演算法為基礎的模型（博士論文）。取自http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dnclcdr&s=id=%22089NCU00396001%22.&searchmode=basic。
4. Adomavicius, G., & Tuzhilin, A. (2005). 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, 17(6), 734-749.
5. Adomavicius, G., & Zhang, J. (2012). Stability of recommendation algorithms. ACM Transactions on Information Systems, 30(4), 1-31. doi: 10.1145/2382438.2382442
6. Balabanović, M., & Shoham, Y. (1997). Fab: Content-based, collaborative recommendation. Communications of the ACM, 40(3), 66-72.
7. Belkin, N. J., & Croft, W. B. (1992). Information filtering and information retrieval: Two sides of the same coin [Special issue]? Communications of the ACM, 35(12), 29-38. doi: 10.1145/138859.138861
8. Blecker, T., Friedrich, G., Kaluza, B., Abdelkafi, N., & Kreutler, G. (2005). Information and management systems for product customization. Springer.
9. Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109-132. doi: 10.1016/j.knosys.2013.03.012
10. Breese, J. S., Heckerman, D., & Kadie, C. (1998, May). Empirical analysis of predictive algorithms for collaborative filtering. Paper presented at the Proceedings of the Fourteenth Conference Conference on Uncertainty in Artificial Intelligence, Madison, Wisconsin (WI).
11. Brintrup, A. M., Ramsden, J., Takagi, H., & Tiwari, A. (2008). Ergonomic chair design by fusing qualitative and quantitative criteria using interactive genetic algorithms. IEEE Transactions on Evolutionary Computation, 12(3), 343-354. doi: 10.1109/TEVC.2007.904343
12. Bryman, A., & Cramer, D. (1997). Quantitative data analysis with spss for windows: A guide for social scientists. Routledge.
13. Buchanan, L., & O`Connell, A. (2006). A brief history of decision making. Harvard Business Review, 84, 32-41.
14. Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4), 331-370. doi: 10.1023/A:1021240730564
15. Bush, B. J., & Sayama, H. (2011). Hyperinteractive evolutionary computation. IEEE Transactions on Evolutionary Computation, 15(3), 424-433. doi: 10.1109/TEVC.2010.2096539
16. Cacheda, F., Carneiro, V., Fernández, D., & Formoso, V. (2011). Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Transactions on the Web, 5(1), 1-33. doi: 10.1145/1921591.1921593
17. Caldwell, C., & Johnston, V. S. (1991, July). Tracking a criminal suspect through "face-space" with a genetic algorithm. Paper presented at the Proceedings of Fourth International Conference on Genetic Algorithms, Morgan Kauffman.
18. Carey, G. (2012). Quantitative methods in neuroscience. Retrieved from http://psych.colorado.edu/~carey/qmin/qmin.php
19. Chay, Z. E., Lee, C. H., Lee, K. C., Oon, J. S., & Ling, M. H. (2010). Russel and rao coefficient is a suitable substitute for dice coefficient in studying restriction mapped genetic distances of escherichia coli. Computational and Mathematical Biology, 1, 1-9.
20. Cheng, C., & Kosorukoff, A. (2004). Interactive one-max problem allows to compare the performance of interactive and human-based genetic algorithms. In K. Deb (Ed.), Genetic and evolutionary computation - GECCO 2004 (Vol. 3102, pp. 983-993): Springer Berlin Heidelberg.
21. Cho, S. B. (2002). Towards creative evolutionary systems with interactive genetic algorithm. Applied Intelligence, 16(2), 129-138. doi: 10.1023/A:1013614519179
22. Choi, S. S., Cha, S. H., & Tappert, C. C. (2010). A survey of binary similarity and distance measures. Journal of Systemics, Cybemetics and Informatics, 8(1), 43-48.
23. Coates, J. F., & Wolff, M. F. (1995). Customization promises sharp competitive edge. Research Technology Management, 38(6), 6-7.
24. de Campos, L. M., Fernández-Luna, J. M., Huete, J. F., & Rueda-Morales, M. A. (2010). Using second-hand information in collaborative recommender systems. Soft Computing, 14(8), 785-798. doi: 10.1007/s00500-009-0474-5
25. da Silveira, G., Borenstein, D., & Fogliatto, F. S. (2001). Mass customization: Literature review and research directions. International Journal of Production Economics, 72(1), 1-13. doi: http://dx.doi.org/10.1016/S0925-5273(00)00079-7
26. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
27. Davis, S. M. (1987). Future perfect. Addison-Wesley, Reading, MA.
28. DeVellis, R. F. (1991). Scale development: Theory and applications. Newbury Park, CA: Sage Publications.
29. Fan, H., & Poole, M. S. (2006). What is personalization? Perspectives on the design and implementation of personalization in information systems. Journal of Organizational Computing and Electronic Commerce, 16(3-4), 179-202.
30. Fogliatto, F. S., da Silveira, G. J. C., & Borenstein, D. (2012). The mass customization decade: An updated review of the literature. International Journal of Production Economics, 138(1), 14-25. doi: http://dx.doi.org/10.1016/j.ijpe.2012.03.002
31. Formoso, V., Fernández, D., Cacheda, F., & Carneiro, V. (2013). Using profile expansion techniques to alleviate the new user problem. Information Processing & Management, 49(3), 659-672. doi: http://dx.doi.org/10.1016/j.ipm.2012.07.005
32. Franke, N., Keinz, P., & Schreier, M. (2008). Complementing mass customization toolkits with user communities: How peer input improves customer self-design. Journal of Product Innovation Management, 25(6), 546-559.
33. Garcia-Hernandez, L., Salas-Morera, L., & Arauzo-Azofra, A. (2012, June). An interactive genetic algorithm applied to the design of recycling plants. Paper presented at the 2012 World Automation Congress (WAC).
34. Gay, L. R. (1996). Educational research: Competencies for analysis and applications. Englewood Cliffs, New Jersey: Prentice-Hall.
35. Geyer-Schulz, A., Hahsler, M., & Jahn, M. (2000). myVU: A next generation recommender system based on observed consumer behavior and interactive evolutionary algorithms. In W. Gaul, O. Opitz, & M. Schader (Eds.), Data Analysis (pp. 447-457): Springer Berlin Heidelberg.
36. Gilmore, J. H., & Pine II, B. J. (1997). The four faces of mass customization. Harvard Business Review, 75(1), 91-101.
37. Goldberg, D. E. (2002). The design of innovation: Lessons from and for competent genetic algorithms. Kluwer Academic Publishers.
38. Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry [Special issue]. Communications of the ACM, 35(12), 61-70. doi: 10.1145/138859.138867
39. Gong, D., Yao, X., & Yuan, J. (2009). Interactive genetic algorithms with individual fitness not assigned by human. Journal of Universal Computer Science, 15(13), 2446-2462.
40. Hicks, D. (2003). Supporting personalization and customization in a collaborative setting. Computers in Industry, 52(1), 71-79. doi: http://dx.doi.org/10.1016/S0166-3615(03)00070-8.
41. Hogan, J.E., Lemon, K.N., & Rust, R.T. (2002). Customer equity management charting new directions for the future of marketing. Journal of Service Research, 5(1), pp.4-12.
42. Holtzschue, L. (2006). Understanding color: An introduction for designers (4th ed.). Wiley.
43. Hsieh, H. F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277-1288. doi: 10.1177/1049732305276687
44. Jacoby, J. (1984). Perspectives on information overload. Journal of Consumer Research, 10(4), 432-436.
45. JakSa, R., & Takagi, H. (2003, October). Tuning of image parameters by interactive evolutionary computation. Paper presented at the 2003 IEEE International Conference on Systems, Man, and Cybernetics.
46. Jeppesen, L. B. (2005). User toolkits for innovation: Consumers support each other. Journal of Product Innovation Management, 22(4), 347-362.
47. Jung, J., Matsuba, Y., Mallipeddi, R., Funaya, H., Ikeda, K., & Minho, L. (2013, October). Evolutionary programming based recommendation system for online shopping. Paper presented at the 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA).
48. Kamalian, R., Zhang, Y., Takagi, H., & Agogino, A. M. (2005, August). Reduced human fatigue interactive evolutionary computation for micromachine design. Paper presented at the Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, China.
49. Kant, V., & Bharadwaj, K. (2013). A user-oriented content based recommender system based on reclusive methods and interactive genetic algorithm. In J. C. Bansal, P. K. Singh, K. Deep, M. Pant, & A. K. Nagar (Eds.), Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012) (Vol. 201, pp. 543-554): Springer India.
50. Kim, H. N., Ha, I., Lee, K. S., Jo, G. S., & El-Saddik, A. (2011). Collaborative user modeling for enhanced content filtering in recommender systems. Decision Support Systems, 51(4), 772-781. doi: 10.1016/j.dss.2011.01.012
51. Kim, H. T., & Ahn, C. W. (2012). An interactive evolutionary approach to designing novel recommender systems. International Journal of Physical Sciences, 7(15), 2237-2338. doi: 10.5897/IJPS11.1599
52. Kim, H. T., Lee, J. H., & Ahn, C. W. (2011). A recommender system based on interactive evolutionary computation with data grouping. Procedia Computer Science, 3(0), 611-616. doi: http://dx.doi.org/10.1016/j.procs.2010.12.102
53. Koga, S., Inoue, T., & Fukumoto, M. (2013, July). A proposal for intervention by user in interactive genetic algorithm for creation of music melody. Paper presented at the 2013 International Conference on Biometrics and Kansei Engineering (ICBAKE).
54. Koletsis, P., & Petrakis, E. M. (2010). SIA: Semantic image annotation using ontologies and image content analysis. In A. Campilho & M. Kamel (Eds.), Image analysis and recognition (Vol. 6111, pp. 374-383): Springer Berlin Heidelberg.
55. Kosorukoff, A. (2001, October). Human based genetic algorithm. Paper presented at the 2001 IEEE International Conference on Systems, Man and Cybernetics.
56. Kotler, K. (1989). From mass marketing to mass customization. Strategy & Leadership, 17(5), 10-47. doi: 10.1108/eb054267
57. Kotler, P., Kartajaya, H., & Setiawan, I. (2010). Marketing 3.0: From products to customers to the human spirit. Wiley, New York.
58. Lai, H. H., Lin, Y. C., Yeh, C. H., & Wei, C. H. (2006). User-oriented design for the optimal combination on product design. International Journal of Production Economics, 100(2), 253-267. doi: http://dx.doi.org/10.1016/j.ijpe.2004.11.005
59. Lam, X. N., Vu, T., Le, T. D., & Duong, A. D. (2008, January). Addressing cold-start problem in recommendation systems. Paper presented at the Proceedings of the 2nd international conference on Ubiquitous information management and communication, Suwon, Korea.
60. Lampel, J., & Mintzberg, H. (1996). Customizing Customization. Sloan Management Review, 38(1), 21-30.
61. Lee, J. Y., & Cho, S. B. (1999, August). Sparse fitness evaluation for reducing user burden in interactive genetic algorithm. Paper presented at the 1999 IEEE International Fuzzy Systems Conference Proceedings.
62. Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1), 76-80. doi: 10.1109/MIC.2003.1167344
63. Luo, T., Xiong, Z., & Fang, Y. (2008, December). A framework of knowledge management for mass customization internet-based. Paper presented at the 2008 International Symposium on Information Science and Engieering.
64. MacCarthy, B., Brabazon, P. G., & Bramham, J. (2003). Fundamental modes of operation for mass customization. International Journal of Production Economics, 85(3), 289-304.
65. Man, K. F., Tang, K. S., & Kwong, S. (2001). Genetic algorithms: Concepts and designs (2nd ed.). Springer.
66. Mandl, M., Felfernig, A., & Tiihonen, J. (2011, September). Evaluating design alternatives for feature recommendations in configuration systems. Paper presented at the 2011 IEEE 13th Conference on Commerce and Enterprise Computing (CEC).
67. Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50, 370-396.
68. Masui, T. (1992, September). Graphic object layout with interactive genetic algorithms. Paper presented at the 1992 IEEE Workshop on Visual Languages Proceedings.
69. Melville, P., Mooney, R. J., & Nagarajan, R. (2002, July). Content-boosted collaborative filtering for improved recommendations. Paper presented at the Eighteenth national conference on Artificial intelligence, Edmonton, Alberta, Canada.
70. Milicevic, A., Nanopoulos, A., & Ivanovic, M. (2010). Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions. Artificial Intelligence Review, 33(3), 187-209. doi: 10.1007/s10462-009-9153-2
71. Nguyen, A. T., Denos, N., & Berrut, C. (2007, October). Improving new user recommendations with rule-based induction on cold user data. Paper presented at the Proceedings of the 2007 ACM conference on Recommender systems, Minneapolis, MN, USA.
72. Ochi, P., Rao, S., Takayama, L., & Nass, C. (2010). Predictors of user perceptions of web recommender systems: How the basis for generating experience and search product recommendations affects user responses. International Journal of Human-Computer Studies, 68(8), 472-482. doi: 10.1016/j.ijhcs.2009.10.005
73. Papagelis, M., Plexousakis, D., & Kutsuras, T. (2005, May). Alleviating the sparsity problem of collaborative filtering using trust inferences. Paper presented at the Proceedings of the Third international conference on Trust Management, Paris, France.
74. Park, D. H., Kim, H. K., Choi, I. Y., & Kim, J. K. (2012). A literature review and classification of recommender systems research. Expert Systems with Applications, 39(11), 10059-10072. doi: http://dx.doi.org/10.1016/j.eswa.2012.02.038
75. Park, S. T., & Chu, W. (2009, October). Pairwise preference regression for cold-start recommendation. Paper presented at the Proceedings of the third ACM conference on Recommender Systems, New York, New York, USA.
76. Peppers, D., & Rogers, M. (1993). The one to one future: Building relationships one customer at a time. New York: Doubleday.
77. Pérez-Gallardo, Y., Alor-Hernández, G., Cortes-Robles, G., & Rodríguez-González, A. (2013). Collective intelligence as mechanism of medical diagnosis: The iPixel approach. Expert Systems with Applications, 40(7), 2726-2737. doi: http://dx.doi.org/10.1016/j.eswa.2012.11.020
78. Piller, F. (2000). Mass customization: Ein wettbewerbsstrategisches Konzept im Informationszeitalter. Deutscher Universitätsverlag.
79. Pine II, B. J. (1993). Mass customization: The new frontier in business competition. Boston, MA: Harvard Business School Press.
80. Prahalad, C. K., & Ramaswamy, V. (2004). The future of competition: Co-creating unique value with customers. Harvard Business School Press.
81. Rao, K. N., & Talwar, V. G. (2008). Application domain and functional classification of recommender systems - A survey. DESIDOC Journal of Library & Information Technology, 28(3), 17-35.
82. Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56-58.
83. Riemer, K., & Totz, C. (2001). The many faces of personalization. In M. M. Tseng., & F. T. Piller (Eds.), Proceedings of the 2001 World Conference on Mass Customization and Personalization. Hong Kong: Hong Kong University of Science and Technology.
84. Riquelme, H. (2001). Do consumers know what they want? Journal of Consumer Marketing, 18(5), 437-448.
85. Rodriguez, L., Diago, L., & Hagiwara, I. (2010, July). Color recommendation system combining design concepts with interactive customers preference modeling from context changes. Paper presented at the 2010 IEEE Congress on Evolutionary Computation (CEC).
86. Rosenberg, N. (1982). Inside the black box: Technology and economics. New York: Cambridge University Press.
87. Schafer, J. B., Konstan, J., and Riedl, J. (1999, November). Recommender systems in e-commerce. Proceedings of the ACM Conference on Electronic Commerce (EC-99).
88. Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M. (2002, August). Methods and metrics for cold-start recommendations. Paper presented at the Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, Tampere, Finland.
89. Schmitt, B., & Simonson, A. (1997). Marketing aesthetics: The strategic management of brands, identity, and Image. New York: The Free Press.
90. Simon, H. A. (1957). Models of man, social and rational: Mathematical essays on rational human behavior in a social setting. New York: John Wiley and Sons.
91. Simon, H. A. (1960). The new science of management decision. New York: Harper & Row.
92. Smith, A. R. (1978). Color gamut transform pairs. ACM SIGGRAPH Computer Graphics, 12(3), 12-19. doi: 10.1145/800248.807361
93. Smith, W. (1956). Product differentiation and market segmentation as alternative marketing strategies. Journal of Marketing, 21, 3-8.
94. Sorn, D., & Rimcharoen, S. (2013, September). Web page template design using interactive genetic algorithm. Paper presented at the 2013 International Computer Science and Engineering Conference (ICSEC).
95. Sumi, S., Oinuma, J., Arakawa, K., & Harashima, H. (2012, November). Interactive evolutionary image processing for face beautification using smaller population size. Paper presented at the 2012 IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2012).
96. Sun, X., Yang, L., Gong, D., & Li, M. (2012, June). Interactive genetic algorithm assisted with collective intelligence from group decision making. Paper presented at the 2012 IEEE Congress on Evolutionary Computation (CEC).
97. Sunikka, A., & Bragge, J. (2012). Applying text-mining to personalization and customization research literature – Who, what and where? Expert Systems with Applications, 39(11), 10049-10058. doi: http://dx.doi.org/10.1016/j.eswa.2012.02.042
98. Takagi H. (2001). Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation, Proceedings of the IEEE, 89(9), 1275-1296.
99. Takagi, H. (2012). Interactive evolutionary computation for analyzing human awareness mechanisms. Applied Computational Intelligence and Soft Computing, 2012, 8 pages. doi: 10.1155/2012/694836
100. Takagi, H., & Ohsaki, M. (2007). Interactive evolutionary computation-based hearing aid fitting. IEEE Transactions on Evolutionary Computation, 11(3), 414-427. doi: 10.1109/TEVC.2006.883465
101. Takagi, H., Takahashi, T., & Aoki, K. (2004, October). Applicability of interactive evolutionary computation to mental health measurement. Paper presented at the 2004 IEEE International Conference on Systems, Man and Cybernetics.
102. Tam, K. Y., & Ho, S. Y. (2006). Understanding the impact of web personalization on user information processing and decision outcomes. MIS Quarterly, 30(4), 865-890.
103. Tanaka, M., Hiroyasu, T., Miki, M., Sasaki, Y., Yoshimi, M., & Yokouchi, H. (2010, July). Automatic generation method to derive for the design variable spaces for interactive genetic algorithms. Paper presented at the 2010 IEEE Congress on Evolutionary Computation (CEC).
104. Tang, Y., & Meersman, R. (2012). DIY-CDR: an ontology-based, Do-It-Yourself component discoverer and recommender. Personal and Ubiquitous Computing, 16(5), 581-595. doi: 10.1007/s00779-011-0416-y
105. Terveen, L. G., & Hill, W. (2001). Human-computer collaboration in recommender systems. in Carroll, J. (Ed.), HCI in the New Millennium. Addison Wesley.
106. Thomke, S., & von Hippel, E. (2002). Customers as innovators: A new way to create value. Harvard Business Review, 80, 74-81.
107. von Hippel, E. (1986). Lead users: A source of novel product concepts. Management Science, 32(7), 791-805.
108. von Hippel, E. (2005). Open source software projects as user innovation networks. In J. Feller, B. Fitzgerald, S. A. Hissam & K. R. Lakhani (Eds.), Perspectives on Free and Open Source Software. Cambridge, Massachusetts, London, England: The MIT Press.
109. Wind, J., & Rangaswamy, A. (2001). Customerization: The next revolution in mass customization. Journal of Interactive Marketing, 15(1), 13-32.
110. Yuliana, P. G., Giner, A. H., Guillermo, C.R., & Alejandro, R.G. (2013). Collective intelligence as mechanism of medical diagnosis: The iPixel approach. Expert Systems with Applications, 40(7), 2726-2737. doi: http://dx.doi.org/10.1016/j.eswa.2012.11.020
111. Zhang, Z. K., Zhou, T., & Zhang, Y. C. (2011). Tag-aware recommender systems: A state-of-the-art survey. Journal of Computer Science and Technology, 26(5), 767-777. doi: 10.1007/s11390-011-0176-1 |
| Description: | 博士
國立政治大學
資訊管理研究所
94356507
102 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0094356507 |
| Data Type: | thesis |
| Appears in Collections: | [資訊管理學系] 學位論文
|
Files in This Item:
| File |
Size | Format | |
|---|
| 650701.pdf | 5038Kb | Adobe PDF2 | 553 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
