在網路搜尋上，對於不同背景與需求的使用者，一個相同的查詢句，所得到的搜尋結果卻都是相同的大量網頁，這使得個人化搜尋的需求越來越高。使用者興趣檔描述了一個特定使用者的興趣，通常用來幫助搜尋引擎提供個人化的搜尋結果或者應用在推薦系統上。在過去的研究中，使用者興趣檔大部分是依照使用者的瀏覽歷史所建立而成的，而此種使用者興趣檔所代表的資訊，主要為使用者長期的資訊需求，而非單次檢索的資訊需求。
本研究提出了一套方法，藉由相關回饋來擷取出使用者的查詢概念，並應用概念擷取的技術來建立概念化的使用者興趣檔，以此改善反映使用者長期性資訊需求的興趣檔在單次資訊檢索中可能檢索出與使用者資訊需求不符的情況，並希冀能協助使用者從大量的搜尋結果中找出與其資訊需求相關的網頁。

In web search, users usually get the same results for the same query, even if they have different interests and backgrounds. It makes the increase of the demand for personalized search increase. A user profile is the description of a specific user interest. It has been used by search engines to provide personalized search results or applied in recommending system. In the past, personalized search usually relies on searching history for personal interest extraction.
In this research we have tried to apply relevance feedback to extract user’s information needs, and apply the technology of concept extraction in the construction of conceptual user profile. It aims to help users to find out the related webpages in numerous search results.

[1] A. Lavelli, F. Sebastiani & R. Zanoli, “Distributional Term Representations: An Experimental Comparison,” Proceedings of the thirteenth ACM international conference on Information and knowledge management, Washington, DC, USA pp. 615-624, 2004.
[2] D. K. Limbu, A. Connor, R. Pears & S. MacDonell, “Contextual relevance feedback in web information retrieval,” Proceedings of the 1st international conference on Information interaction in context, Copenhagen, Denmark, pp. 138-143, 2006.
[3] F. Liu, C. Yu & W. Meng, “Personalized Web Search by Mapping User Queries to Categories,” Proceedings of the eleventh international conference on Information and knowledge management, McLean, Virginia, USA , pp. 558-565, 2002.
[4] G. Salton & M. E. Lesk, “Computer Evaluation of Indexing and Text Processing,” Journal of the ACM, 15(1), pp. 8-36, 1968.
[5] G. Salton & C. Buckley, “Term-Weighting Approaches in Automatic Text Retrieval,” Information Processing and Management, 24(5), pp. 513-523, 1988.
[6] H. R. Kim & P. K. Chan, “Learning Implicit User Interest Hierarchy for Context in Personalization,” Proceedings of the 8th international conference on Intelligent user interfaces, Miami, Florida, USA, pp. 101-108, 2003.
[7] J. Trajkova & S. Gauch, “Improving Ontology-Based User Profiles,” Proceedings of RIAO, University of Avignon: Vaucluse (France), 26-28, pp. 380-389, 2004.
[8] L. Karoui, M.A. Fufaure and N. Bennacer, “A New Extraction Concept based on Contextual Clustering,” Proceedings of the International Conference on Computational Intelligence for Modeling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce, Washington, DC, USA, pp.91, 2006.
[9] M. D. Mulvenna, S. S. Anand & Buchner A.G., “Personalization on the Net using Web Mining,” Comm. Of the ACM, 43(8), pp. 123-125, 2000.
[10] M. Rahman, Chowdhury, Ferdous Ahmed Sohel, Parvez Naushad, & S. M. Kamruzzaman, “Text Classification using the Concept of Association Rule of Data Mining,” Proceeding of the International Conference on Information Technology, Maribor, Slovenia, pp. 23-26 ,2003.
[11] M. Sugimoto, “User Modeling and Adaptive Interaction in Information Gathering System,” Journal of Japanese Society for Artificial Intelligence, 14(1), pp. 25-32, 1999.
[12] M. Speretta & S. Gauch, “Personalized Search Based on User Search Histories,” Proceedings of the 2005 IEEE/WIC/ACM international Conference on Web intelligence, Washington, DC, pp. 622-628, 2005.
[13] P. Soucy & G. W. Mineau, “Beyond TFIDF Weighting for Text Categorization in the Vector Space Model,” International Joint Conference on Artificial Intelligence, Edinburgh, Scotland, UK, pp. 1130-1135, 2005.
[14] R. Baeza-Yates & B. Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley Longman, pp. 433-436, 1999.
[15] R. Lecceuche, “Finding Comparatively Important Concepts between Texts,” Proceedings of the 15th IEEE international conference on Automated software engineering, Washington, DC, USA, pp.55, 2000.
[16] V. Fresno & A. Ribeiro, “An Analystical Approach to Concept Extraction in HTML Environments,” Journal of Intelligent Information Systems, 22(3), pp. 215-235, 2004.
[17] V. Fresno, R. Martinez & S. Montalvo, “Improving Web Page Clustering Through Selecting Appropriate Term Weighting Functions,” Proceedings of the Web Mining Workshop into the IEEE 1st International Conference on Digital Information Management, Bangalore, India, pp. 511-518, 2006.
[18] Y. Chang, M. Kim & V. V. Raghavan, “Construction of Query Concepts Based on Feature Clustering of Documents,” Information Retrieval, 9(3), pp. 231-248, 2006.
[19] Z. N. Zacharis & T. Panayiotopoulos, “Web Search Using a Genetic Algorith,” IEEE Internet Computing, 5(2), pp. 18-26, 2001.
[20] D. Sullivan, “The order you are, the more you want personalized search,” 2004, http://searchenginewatch.com/searchday/article.php/3385131