Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/32733
|
| Title: | 利用隱含回饋提供搜尋引擎的自動查詢修正
Automatic Query Refinement in Web Search Engines using Implicit Feedback |
| Authors: | 彭冠誌
Peng，Kuan-Chih |
| Contributors: | 沈錳坤
Shan，Man-Kwan
彭冠誌
Peng，Kuan-Chih |
| Keywords: | 查詢修正
隱含回饋
搜尋引擎生手
長期情境
短期情境
Query Refinement
Implicit Feedback
Novice User
Long-term Context
Short-term Context |
| Date: | 2006 |
| Issue Date: | 2009-09-17 14:09:42 (UTC+8) |
| Abstract: | 隨著全球資訊網蓬勃的發展，可以幫助使用者根據關鍵字搜尋相關資訊的搜尋引擎也已變成使用者不可或缺的工具之一。但對於搜尋引擎生手而言，往往不知道該如何地輸入適當的關鍵字，導致搜尋結果不如預期。如果搜尋引擎可以提供自動查詢修正(Automatic Query Refinement)的功能，將可以有效地幫助生手在網路上找尋到其想要的資訊。因此，如何得知使用者的資訊需求，如何自動化地達到查詢修正，則成為重要的課題之一。本研究利用使用者的隱含回饋(Implicit Feedback)來分析使用者的資訊需求，並探勘過去具有相同資訊需求的使用者經驗，以幫助搜尋引擎生手有效地搜尋網頁，以達到自動查詢修正的目的。
本研究中，在長期情境資訊方面，我們從查詢日誌中去辨別出以往使用者所查詢的關鍵字以及點選過的網頁，接著，在短期情境資訊的部份，我們也記錄下目前使用者的查詢關鍵字以及未點選之網頁。
最後，我們在長期情境中濾除掉搜尋引擎生手的查詢過程，同時探勘出與目前使用者有相似資訊需求的以往經驗使用者之查詢過程關鍵字集合，藉以推薦給目前使用者，完成自動查詢修正。
World Wide Web search engines can help users to search information by their queries, but novice search engines users usually don’t know how to represent their information need. If search engines can offer query refinement automatically, it will help novice search engine users to satisfy their information need effectively. How to find users’ information need, and how to perform query refinement automatically, have become important research issues. In this thesis, we develop the method to help novice search engine users for satisfying their information need effectively by implicit feedback. Implicit feedback in this research is referring to short-term context and long-term context.
In this research, first, long-term context is obtained by identifying each user’s queries and extracting conceptual keywords of clickthrough data in each query session from query logs. Then, we also identify current user’s queries and extract conceptual keywords of non-clickthrough data for short-term context identification.
Finally, we filter novice sessions from long-term context, and mine frequent itemsets of past experienced users’ search behavior to suggest the most appropriate new query to current user according to their information need. |
| Reference: | [1] R. Agrawal, and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proceedings of International Conference on Very Large Databases, 1994.
[2] C. H. Chang, and C.C. Hsu, “Multi-Engine Search Tool with Clustering,” Proceedings of International World Wide Web Conference, 1997.
[3] C. H. Chang, and C. C. Hsu, “Integrating Query Expansion and Conceptual Relevance Feedback for Personalized Web Information Retrieval,” Proceedings of International World Wide Web Conference WWW, 1998.
[4] M. F. Chen, “Ontology Learning from Query Logs of Search Engine,” Master Thesis, National Chengchi University, TW, 2003.
[5] M. S. Chen, J. S. Park, and P.S. Yu, “Efficient Data Mining for Path Traversal Patterns,” IEEE Transaction on Knowledge and Data Engineering TKDE, Vol. 10, No. 2, 1998.
[6] H. Cui, J. R. Wen, J. Y. Nie, and W. Y. Ma, “Query Expansion by Mining User Logs,” IEEE Transactions on Knowledge and Data Engineering, Vol.15, No.4, 2003.
[7] N. Eiron, and K. S. McCurley, “Analysis of Anchor Text for Web Search,” Proceedings of ACM International Conference on Research and Development in Information Retrieval SIGIR, 2003.
[8] B. M. Fonseca, P. Golgher, and B. Pôssas, “Concept-Based Interactive Query Expansion,” Proceedings of ACM International Conference on Information and Knowledge Management CIKM, 2005.
[9] A. Gomez-Perez, M. Fernandez-Lopez, and O. Corcho, “Ontological Engineering: with Examples from the Areas of Knowledge Management,” E-Commerce and the Semantic Web, Springer-Verlag, 2002.
[10] J. Hartmann, N. Stojanovic, R. Studer, and L. S. Thieme, “Ontology-Based Query Refinement for Semantic Portals,” Proceedings of Integrated Publication and Information Systems to Virtual Information and Knowledge Environments, 2005.
[11] C. Holscher, and G. Strube, “Web Search Behavior of Internet Experts and Newbies,” Computer Networks, Vol.33, No.22, 2000.
[12] E. Ide, “New Experiment in Relevance Feedback,” The SMART Retrieval System, 1971.
[13] T. Joachims, “Optimizing Search Engines Using Clickthrough Data,” Proceedings of ACM International Conference on Data Mining and Knowledge Discovery SIGKDD, 2002.
[14] D. Kelly, and N. J. Belkin, “Display Time as Implicit Feedback: Understanding Task Effects,” Proceedings of ACM International Conference on Research and Development in Information Retrieval SIGIR, 2004.
[15] D. Kelly, and J. Teevan, “Implicit Feedback for Inferring User Preference,” SIGIR Forum, Vol.32, No.2, 2003.
[16] R. Kraft, and J. Zien, “Mining Anchor Text for Query Refinement,” Proceedings of International World Wide Web Conference WWW, 2004.
[17] T. Lau, and E. Horvitz, “Patterns of Search: Analyzing and Modeling Web Query Refinement,” Proceedings of the ACM International Conference on User Modeling, 1998.
[18] U. Lee, Z. Liu, and J. Cho, “Automatic Identification of User Goals in Web Search,” Proceedings of International World Wide Web Conference WWW, 2005.
[19] C. C. Lin, and M. S. Chen, “VIPAS: Virtual Link Powered Authority Search in the Web,” Proceedings of the International Conference on Very Large Data Bases VLDB, 2003.
[20] H. Liu, H. Lieberman, and T. Selker, “GOOSE: A Goal-Oriented Search Engine with Commonsense,” Proceedings of 2nd International Conference on Adaptive Hypermedia and Adaptive Web Based Systems, 2002.
[21] K. Noriaki, M. Takeya, and H. Miyoshi, “Semantic Log Analysis Based on a User Query Behavior Model,” Proceedings of IEEE International Conference on Data Mining ICDM, 2003.
[22] F. Radlinski, and T. Joachims, “Query Chains: Learning to Rank from Implicit Feedback,” Proceedings of ACM International Conference on Data Mining and Knowledge Discovery SIGKDD, 2005.
[23] J. J. Rocchio, and K. S. Jones, “Relevance Feedback in Information Retrieval,” The SMART Retrieval System-Experiment in Automatic Document Processing. Prentice Hall Inc., Englewood Cliffs, NJ, 1971.
[24] D. E. Rose, and D. Levinson, “Understanding User Goals in Web Search,” Proceedings of International World Wide Web Conference WWW, 2004.
[25] X. Shen, B. Tan, and C. Zhai, “Context-Sensitive Information Retrieval Using Implicit Feedback,” Proceedings of ACM International Conference on Research and Development in Information Retrieval SIGIR, 2005.
[26] X. Shi, and C. C. Yang, “Mining Related Queries from Search Engine Query Logs” Proceedings of International World Wide Web Conference WWW, 2006.
[27] P. Singh, “The Public Acquisition of Commonsense Knowledge,” Proceedings of American Association for Artificial Intelligence AAAI, 2002.
[28] A. Spink, B. J. Jansen, D. Wolfram, and T. Saracevic, “From E-Sex to E- Commerce: Web Search Changes,” IEEE Computer, Vol.35, No.3, 2002.
[29] N. Stojanovic, “On the Query Refinement in the Ontology-Based Searching for Information,” Proceedings of Conference on Advanced Information Systems Engineering, 2005.
[30] K. Sugiyama, K. Hatano, and M. Yoshikawa, “Adaptive Web Search Based on User Profile Constructed without Any Effort from Users,” Proceedings of International World Wide Web Conference WWW, 2004.
[31] R. W. White, J. M. Jose, C. J. van Rijsbergen, and I. Ruthven, “A Simulated Study of Implicit Feedback Models,” Proceedings of European Conference on Information Retrieval, 2004.
[32] R. W. White, I. Ruthven, and J. M. Jose, “A Study of Factors Affecting the Utility of Implicit Relevance Feedback” Proceedings of ACM International Conference on Research and Development in Information Retrieval SIGIR, 2005.
[33] D. H. Widyantoro and J. Yen, “A Fuzzy Ontology-based Abstract Search Engine and Its User Studies,” Proceedings of IEEE International Conference on Fuzzy Systems, 2001.
[34] D. H. Widyantoro, and J. Yen, “Using Fuzzy Ontology for Query Refinement in a Personalized Abstract Search Engine,” Proceedings of Investment and Financial Services Association, 2001.
[35] K.J. Wu, M. C. Chen, and Y. Sun, “Automatic Topics Discovery from Hyperlinked Documents,” Information Processing and Management, Vol.40, No.4, 2004.
[36] C. Zhai, and J. Lafferty, “Model-Based Feedback in the KL-Divergence Retrieval Model,” Proceedings of ACM International Conference on Information and Knowledge Management CIKM, 2001. |
| Description: | 碩士
國立政治大學
資訊科學學系
93753033
95 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0937530331 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系] 學位論文
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
