政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/32694

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/32694

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政大典藏 > College of Informatics > Department of Computer Science > Theses > Item 140.119/32694

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/32694

Title:	對於閱讀的感興趣程度與眼動特徵關係之研究 The Research on the Relationship between Interesting Degree of Reading and Eye Movement Features
Authors:	王加元 Wang, Jia Yuan
Contributors:	陳良弼蔡介立 Chen, Arbee L.P. Tsai, Jie Li 王加元 Wang, Jia Yuan
Keywords:	眼動閱讀感興趣程度資料探勘序列資料探勘重複片段分類 eye movement reading interesting degree data mining sequence mining repeating pattern classification
Date:	2008
Issue Date:	2009-09-17 14:04:29 (UTC+8)
Abstract:	現在有許多對於眼動軌跡與人在認知方面的研究，包括理解狀態以及感興趣的程度；其中，閱讀文章時的眼動軌跡是最常被討論及研究的題材。而本研究的目的就是希望探討讀者在閱讀時的眼動軌跡，與其感興趣程度之間是否存在關係。<br>本研究的特色在於，我們不用一般分析眼動時關心每個AOI（area of interest）上的眼動資料，而是希望將眼動資料以序列的方式分析，並且運用資料探勘的方法，找出眼動序列中區分感興趣程度的眼動軌跡特徵的片段。<br>透過對於眼動軌跡的分析，我們希望研究的結果，在未來可以運用在資訊檢索的領域上，成為一種有效的「隱含式回饋（implicit feedback）」的方式，以改善現有資訊檢索效能。 Much research has been performed on the relationship between eye movements and human cognition, including comprehension and interesting degree. The purpose of our research is to find out if there are relationships between eye movements of reading and interesting degree.<br>Instead of analyzing the eye movements on each area of interest, the characteristic of our research is to transform eye movements to sequence data, and to determine the eye movement patterns which discriminate whether user is interesting or not by using the method of data mining.<br>Through the analysis of the eye movements, our research result can be used as one way of implicit feedback of information retrieval to improve the effectiveness of the search engine.
Reference:	[1] Dario D. Salvucci and Joseph H. Goldberg, “Identifying fixations and saccades in eye-tracking protocols,” Proceedings of the 2000 symposium on Eye tracking research & applications. [2] 蔡介立(Jie-Li Tsai), 顏妙璇(Miao-Hsuan Yen), and 汪勁安(Chin-An Wang), “眼球移動測量及在中文閱讀研究之應用,” 應用心理研究, 28期, 91-104, 2005. [3] Jarkko Salojarvi, Kai Puolamaki, Jaana Simola, Lauri Kovanen, Ilpo Kojo, and Samuel Kaski, “Inferrring Relevance from Eye Movements: Feature Extraction,” Helsinki University of Technology, Publications in Computer and Information Science. [4] Rayner, K., Chace, K. H., Slattery, T. J. and Ashby, J, “Movements as Reflections of Comprehension Processes in Reading,” Scientific Studies of Reading 10(3): 241-255. [5] Aulikki Hyrskykari, Paivi Majaranta, Antti Aaltonen, and Kari-Jouko Raiha, “Design Issues of iDict: A Gaze-Assisted Translation Aid,” Proceedings of the 2000 symposium on Eye tracking research & applications. [6] Bing Pan, Helene A. Hembrooke, Geri K. Gay, Laura A. Granka, Matthew K. Feusner, and Jill K. Newman, “The Determinants of Web Page Viewing Behavior: An Eye-Tracking Study,” Proceedings of the 2004 symposium on Eye tracking research & applications. [7] Julia M. West, Anne R. Haake, Evelyn P. Rozanski, and Keith S. Karn, “eyePattern: Softwore for Identifying Patterns and Similarities Across Fixation Sequences,” Proceedings of the 2006 symposium on Eye tracking research & applications. [8] Hidetake Uwano, Masahide Nakamura, Akito Monden and Ken-ichi Matsumoto, “Analyzing Individual Performance of Source Code Review Using Reviewer’s Eye Movement,” Proceedings of the 2006 symposium on Eye tracking research & applications. [9] Georg Buscher, “Attention-Based Information Retrieval,” ACM SIGIR Conference on Research and Development of Information Retrieval, 2007. [10] David Hardoon, John Shawe-Taylor, Antti Ajanki, Kai Puolamäki, and Samuel Kaski, “Information Retrieval by Inferring Implicit Queries from eye Movements,” Artificial Intelligence and Statistics, 2007. [11] 中央研究院漢語平衡語料庫，http://www.aclclp.org.tw/use_asbc_c.php [12] SHYAMALA DORAISAMY, and STEFAN RÜGER, “Robust Polyphonic Music Retrieval with N-grams,” Journal of Intelligent Information Systems, 21:1, 53–70, 2003. [13] Jia-Lien Hsu, Arbee L.P. Chen, Hung-Chen Chen, “Finding Approximate Repeating Patterns from Sequence Data,” Proc. International Symposium on Music Information Retrieval, 2004.
Description:	碩士國立政治大學資訊科學學系 95753009 97
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0095753009
Data Type:	thesis
Appears in Collections:	[Department of Computer Science ] Theses

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