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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 鄭卜壬 | |
dc.contributor.author | Wei-Lian Chen | en |
dc.contributor.author | 陳威良 | zh_TW |
dc.date.accessioned | 2021-06-15T06:57:35Z | - |
dc.date.available | 2012-02-20 | |
dc.date.copyright | 2011-02-20 | |
dc.date.issued | 2011 | |
dc.date.submitted | 2011-01-28 | |
dc.identifier.citation | [1] ANAND, S. S. AND MOBASHER, B. 2005. Intelligent techniques for web personalization. In Intelligent Techniques for Web Personalization. Bamshad Mobasher and Sarabjot Singh Anand (Eds),Lecture Notes in Artificial Intelligence (3169),Springer, 1–37.
[2] XUE, G. R., LIN, C, YANG, q., XI, W., ZENG, H.-J., YU, Y., AND CHEN, Z. 2005. Scalable collaborative filtering using cluster-based smoothing. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM Press, 114–121. [3] SHAHABI, C. AND CHEN, Y. S. 2003. Web information personalization: Challenges and approaches.In Proceedings of Databases in Networked Information Systems. 5–15. [4] Salton, G. and McGill, M. J. 1983 Introduction to modern information retrieval. McGraw-Hill. [5] Jean-Charles de Borda, who devised the system in June of 1770, invented his system as a fair way to elect members to the French Academy of Sciences, and first published his method in 1781 as Mémoire sur les élections au scrutin in the Histoire de l'Académie Royale des Sciences, Paris. The method was used by the Academy from 1784 until being quashed by Napoleon in 1800. [6] P.-N. Tan, M. Steinbach & V. Kumar, 'Introduction to Data Mining', , Addison-Wesley (2005). [7] Kalervo Jarvelin, Jaana Kekalainen: Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems 20(4), 422–446 (2002). [8] Barry Smyth, Evelyn Balfe, Peter Briggs, Maurice Coyle, Jill Freyne (2003), 'Collaborative Web Search', IJCAI: 1417–1419. [9] D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, “Using collaborative filtering to weave an information tapestry,” Communications of ACM, vol. 35, no. 12, pp. 61–70, 1992. [10] P. Resnick and H. R. Varian, “Recommender systems,” Communications of the ACM, vol. 40, no. 3, pp. 56–58, 1997. [11] Abhinandan S. Das, Mayur Datar, Ashutosh Garg, Shyam Rajaram: Google news personalization: scalable online collaborative filtering. WWW 2007. [12] Huizhi Liang;Yue Xu;Yuefeng Li;Nayak, R.: Collaborative Filtering Recommender Systems Using Tag Information.Web Intelligence and Intelligent Agent Technology IEEE 2008 [13] Schafer, J.B., Konstan, J.A., and Riedl, J. (1999), “Recommender Systems in Electronic Commerce,” Proceedings of the ACM Conference on Electronic Commerce (EC-99). [14] David W. McDonald,Mark S. Ackerman: Expertise Recommender: A Flexible Recommendation System and Architecture. CSCW’00, December 2-6, 2000. [15] Alexander Birukov, Enrico Blanzieri, Paolo Giorgini: Implicit: An AgentBased Recommendation System for Web Search. AAMAS’05, July 2529,2005. [16] Meredith Ringel Morris,Eric Horvitz:SearchTogether: an interface for collaborative web search. UIST 2007. [17] Jeremy Pickens, Gene Golovchinsky, Chirag Shah, Pernilla Qvarfordt, Maribeth Back: Algorithmic mediation for collaborative exploratory search. SIGIR 2008 [18] Osmar R. Zaïane, Alexander Strilets: Finding Similar Queries to Satisfy Searches Based on Query Traces. OOIS 2002. [19] Ricardo Baeza-Yates, Carlos Hurtado, Marcelo Mendoza: Query Recommendation Using Query Logs in Search Engines. EDBT 2004 workshops. [20] Zhiyong Zhang, Olfa Nasraoui:Mining search engine query logs for query recommendation. WWW 2006 [21] Ricardo Baeza-Yates, Carlos Hurtado, Marcelo Mendoza: Improving search engines by query clustering. Journal of the American Society for Information Science and Technology Volume 58 Issue 12, October 2007. [22] Silviu Cucerzan, Ryen W. White: Query suggestion based on user landing pages. SIGIR 2007. [23] Huanhuan Cao et., al: Context-aware query suggestion by mining click-through and session data. KDD 2008. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/48457 | - |
dc.description.abstract | 身處於資訊爆炸的時代,想要在浩瀚的網海中搜尋到真正所需的資料,變得越來越不容易,在如此大量的資料海中,使用者往往會因為自己的知識領域偏狹於某個部分,搜尋時會遺漏掉某些自己想要但不熟悉的資料,亦或是對某個領域認知不足,不知道如何開始進行搜尋該領域的資料,此時,如果能夠在搜尋的過程中,請過去有搜尋過相關領域的經驗者協助,似乎會有助於我們解決所面臨到的問題。
於是,我們想建立一個基於全球資訊網路上的協同式搜尋系統,架構於現有的網路搜尋引擎上,將每個使用者查詢結果的snippet斷詞後,當作為自己的query profile,當使用者搜尋某個關鍵字時,系統依照重要性、相關性及新穎性,會自動推薦與其相似的query profile內之關鍵字當作query,期望能夠幫助使用者加強在全球資訊網上的搜尋效能。 最後我們設計三個實驗,分別針對查詢的效能、推薦字的新穎性及推薦字的相關性來進行驗證及比較,並討論我們方法的優點及未來可精進之處。 | zh_TW |
dc.description.abstract | In the era of information explosion,it becomes more and more difficult to find out the information meeting users’ real needs on the internet.On account of their own limited domain knowledge,users may often overlook the information that they are not familiar with,but need.Users,as a result of insufficient knowledge of some field,may not have any idea how to start searching information,too.Such problems might be solved more easily,as those who have had already experience if searching information in the internet could help users.
A collaborative search system based on the internet would be designed.This collaborative search system,working together with the present search engine,will segment the snippet of search results and take the segmentation for its own query prefile.When users query,the system will also automatically suggest the terms of similar query profile as queries,according to importance,relevance and novelty.The efficiency of searching on the internet could hopefully become better in this way. Finally,there are three experiments designed to examine and evaluate query precision,the novelty and relevance of recommended terms.The strong points and possible improvement of this method will be discussed,too. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T06:57:35Z (GMT). No. of bitstreams: 1 ntu-100-P97922003-1.pdf: 2938844 bytes, checksum: 89a8c9800cdcdbb12d1f68038f1ffa33 (MD5) Previous issue date: 2011 | en |
dc.description.tableofcontents | 口試委員審定書 i
誌謝 ii 中文摘要 iii ABSTRACT iv 目錄 v 圖目錄 vii 表目錄 viii 第1章 序論 1 1.1 前言 1 1.2 什麼是協同式搜尋 1 1.3 研究動機與挑戰 2 1.4 解決方法 3 1.5 章節架構 3 第2章 文獻回顧與相關研究 4 2.1 協同式搜尋的分類 4 2.2 I-SPY 5 2.3 Collaborative Exploratory Search 6 2.4 QUERY SUGGESTION 7 第3章 研究方法 9 3.1 系統概說 9 3.2 個人程序(Local Information) 10 3.2.1 使用者登入 11 3.2.2 擷取詞彙 12 3.2.3 計算詞彙於query profile中的重要性 13 3.2.4 選擇與查詢主題相關資訊 19 3.2.5 如何算出推薦字 23 3.2.6 如何持續且動態找出沒看過的關鍵字 24 3.2.7 最終推薦關鍵字 27 第4章 實驗與討論 29 4.1 實驗資料 29 4.2 實驗設計 30 4.3 實驗一:評估協同式搜尋系統之效能 30 4.3.1 目的及方法 30 4.3.2 實驗結果與討論 30 4.4 實驗二:比較unseen機制對推薦詞彙新穎性的作用 33 4.4.1 目的及方法 33 4.4.2 實驗結果與討論 34 4.5 實驗三:使用者評斷推薦詞彙的相關性 35 4.5.1 目的及方法 35 4.5.2 結果與討論 35 第5章 結論與未來工作 38 5.1 結論 38 5.2 未來工作 38 參考文獻 40 附錄一 42 附錄二 50 | |
dc.language.iso | zh-TW | |
dc.title | 基於全球資訊網之協同式搜尋 | zh_TW |
dc.title | Web-based Collaborative Search | en |
dc.type | Thesis | |
dc.date.schoolyear | 99-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 魏志達,邱志義 | |
dc.subject.keyword | 協同式搜尋,查詢推薦,搜尋, | zh_TW |
dc.subject.keyword | IR,Collaborative search,query suggestion, | en |
dc.relation.page | 59 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2011-01-28 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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