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題名 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
作者 彭冠誌
Peng，Kuan-Chih
貢獻者 沈錳坤
Shan，Man-Kwan
彭冠誌
Peng，Kuan-Chih
關鍵詞 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
日期 2006
上傳時間 17-Sep-2009 14:09:42 (UTC+8)
摘要 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.
參考文獻 [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.
描述 碩士
國立政治大學
資訊科學學系
93753033
95
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0937530331
資料類型 thesis
dc.contributor.advisor 沈錳坤zh_TW
dc.contributor.advisor Shan，Man-Kwanen_US
dc.contributor.author (Authors) 彭冠誌zh_TW
dc.contributor.author (Authors) Peng，Kuan-Chihen_US
dc.creator (作者) 彭冠誌zh_TW
dc.creator (作者) Peng，Kuan-Chihen_US
dc.date (日期) 2006en_US
dc.date.accessioned 17-Sep-2009 14:09:42 (UTC+8)-
dc.date.available 17-Sep-2009 14:09:42 (UTC+8)-
dc.date.issued (上傳時間) 17-Sep-2009 14:09:42 (UTC+8)-
dc.identifier (Other Identifiers) G0937530331en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/32733-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學學系zh_TW
dc.description (描述) 93753033zh_TW
dc.description (描述) 95zh_TW
dc.description.abstract (摘要) 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。		zh_TW
dc.description.abstract (摘要) 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.		en_US
dc.description.tableofcontents 中文摘要…………………………………………………………………………i
英文摘要………………………………………………………………………...ii
目錄……………………………………………………………………………..iii
圖目錄…………………………………………………………………………...v
表目錄…………………………………………………………………………..vi
第一章　緒論…………………………………………………………………...1
第二章　相關研究……………………………………………………………...6
2.1　網路搜尋的查詢修正….………...…………………………………..6
2.2　隱含回饋....…………………………………………………………..9
第三章　研究方法........…………………………………………………….11
3.1　User Session and Query Session Identification…………12
3.2　Implicit Feedback Identification………..……...…………18
3.2.1　Conceptual Keyword Extraction……………..………………19
3.2.2　Long-term Context Identification………………..…….…21
3.2.3　Short-term Context Identification………………..………22
3.3　Filtering………………..………………..………..……………23
3.4　Mining Experienced Keywords…………….….…………………26
第四章　系統實作與實驗評估……………………………………………….29
4.1　系統架構……………………………………………………………29
4.2　實驗資料來源………………………………………………………37
4.3　實驗設計……………………………………………………………38
4.4　實驗評估……………………………………………………………42
4.5　實驗結果……………………………………………………………43
第五章　結論與未來研究方向……………………………………………….47
5.1　結論…………………………………………………………………47
5.2　未來研究方向………………………………………………………48
參考文獻……………………………………………………………………….49		zh_TW
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dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0937530331en_US
dc.subject (關鍵詞) 查詢修正zh_TW
dc.subject (關鍵詞) 隱含回饋zh_TW
dc.subject (關鍵詞) 搜尋引擎生手zh_TW
dc.subject (關鍵詞) 長期情境zh_TW
dc.subject (關鍵詞) 短期情境zh_TW
dc.subject (關鍵詞) Query Refinementen_US
dc.subject (關鍵詞) Implicit Feedbacken_US
dc.subject (關鍵詞) Novice Useren_US
dc.subject (關鍵詞) Long-term Contexten_US
dc.subject (關鍵詞) Short-term Contexten_US
dc.title (題名) 利用隱含回饋提供搜尋引擎的自動查詢修正zh_TW
dc.title (題名) Automatic Query Refinement in Web Search Engines using Implicit Feedbacken_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) [1] R. Agrawal, and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proceedings of International Conference on Very Large Databases, 1994.zh_TW
dc.relation.reference (參考文獻) [2] C. H. Chang, and C.C. Hsu, “Multi-Engine Search Tool with Clustering,” Proceedings of International World Wide Web Conference, 1997.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [4] M. F. Chen, “Ontology Learning from Query Logs of Search Engine,” Master Thesis, National Chengchi University, TW, 2003.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [11] C. Holscher, and G. Strube, “Web Search Behavior of Internet Experts and Newbies,” Computer Networks, Vol.33, No.22, 2000.zh_TW
dc.relation.reference (參考文獻) [12] E. Ide, “New Experiment in Relevance Feedback,” The SMART Retrieval System, 1971.zh_TW
dc.relation.reference (參考文獻) [13] T. Joachims, “Optimizing Search Engines Using Clickthrough Data,” Proceedings of ACM International Conference on Data Mining and Knowledge Discovery SIGKDD, 2002.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [15] D. Kelly, and J. Teevan, “Implicit Feedback for Inferring User Preference,” SIGIR Forum, Vol.32, No.2, 2003.zh_TW
dc.relation.reference (參考文獻) [16] R. Kraft, and J. Zien, “Mining Anchor Text for Query Refinement,” Proceedings of International World Wide Web Conference WWW, 2004.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [24] D. E. Rose, and D. Levinson, “Understanding User Goals in Web Search,” Proceedings of International World Wide Web Conference WWW, 2004.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [26] X. Shi, and C. C. Yang, “Mining Related Queries from Search Engine Query Logs” Proceedings of International World Wide Web Conference WWW, 2006.zh_TW
dc.relation.reference (參考文獻) [27] P. Singh, “The Public Acquisition of Commonsense Knowledge,” Proceedings of American Association for Artificial Intelligence AAAI, 2002.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [29] N. Stojanovic, “On the Query Refinement in the Ontology-Based Searching for Information,” Proceedings of Conference on Advanced Information Systems Engineering, 2005.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW
dc.relation.reference (參考文獻) [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.zh_TW