政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/38544

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/38544

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政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/38544

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/38544

題名:	個人化部落格搜尋 Personalized search from blog data
作者:	黃翊書
貢獻者:	陳良弼 Chen, Arbee L.P. 黃翊書
關鍵詞:	個人化搜尋部落格搜尋社群網路 personalized search blog search social network
日期:	2009
上傳時間:	2010-04-09 13:24:13 (UTC+8)
摘要:	隨著部落格的文章越來多，如何在大量的部落格文章中有效的幫助使用者找到需要的文章就變成一個非常重要的問題。因此，本研究結合部落格搜尋和個人化搜尋的技術，提出針對搜尋部落格文章的個人化部落格搜尋。其中，主要包含了兩種個人化部落格搜尋的方法。首先，延伸一般個人化搜尋的方式針對部落格的環境加以修正，提出了內文個人化部落格搜尋。透過分析和搜尋系統使用者相關的部落格文章內文，讓使用者可以更快的在部落格中找到需要的文章。接著，我們提出創新的社交個人化部落格搜尋，藉由分析使用者和其他部落客之間的社交行為，來達到個人化的目的。最後，在實驗中可以發現，個人化部落格搜尋明顯的提升了部落格搜尋的準確度和滿意度。 As the quantity of articles increased rapidly, how to help the users to find the blog articles effectively becomes an important issue. Therefore, this study combines the traditional blog search and personalized search techniques, and proposes a way to search blog by personal information. This study mainly contains two methods of personalized blog search. First, the general way of personalized search environment for blogs to be amended, the content based personalized blog search. By analyzing and searching the system-related blog article text of the users, this study can make users quickly figure out the articles they need. We propose a social based personalized blog search, by analyzing the social behavior between users and other bloggers, to achieve personal goals. Finally, the result of the experiments has shown that both ways obviously improved search accuracy and satisfaction.
參考文獻:	[1] P. A. Chirita, C. S. Firan, and W. Nejdl. Personalized query expansion for the web. In Proc. of the ACM SIGIR Conf. on Research and Development in Information Retrieval, pages 7–14, 2007. [2] P. A. Chirita, W. Nejdl, R. Paiu, and C. Kohlsch¨utter. Using odp metadata to personalize search. In Proc. of the ACM SIGIR Conf. on Research and Development in Information Retrieval, pages 178–185, 2005. [3] Z. Dou, R. Song, and J.-R. Wen. A large-scale evaluation and analysis of personalized search strategies. In Proc. of ACM Conf. on World Wide Web, pages 581–590, 2007. [4] J. Elsas, J. Arguello, J. Callan, and J. Carbonell. Retrieval and feedback models for blog feed search. In Proc. of the ACM SIGIR Conf. on Research and Development in Information Retrieval, pages 347-354,2008. [5] J. Teevan, S. T. Dumais, and E. Horvitz. Beyond the commons: Investigating the value of personalizing web search. In Proc. of the Workshop on New Technologies for Personalized Information Access, pages 84-92, 2005. [6] K. J¨arvelin and J. Kek¨al¨ainen. Ir evaluation methods for retrieving highly relevant documents. In Proc. of the ACM SIGIR Conf. on Research and Development in Information Retrieval, pages 41–48, 2000. [7] J. Kim, T. Yoon, K. Kim, and J. Lee. Trackback-rank: an effective ranking algorithm for the blog search. In Proc. of the IEEE International Symposium on Intelligent Information Technology Application, pages 503-507,2008. [8] F. Liu, C. Yu, and W. Meng. Personalized web search by mapping user queries to categories. In Proc. of the ACM Conf. on Information and Knowledge Management, pages 558–565, 2002. [9] C. Macdonald and L. Ounis. Key blog distillation: ranking aggregates. In Proc. of the ACM Conf. on Information and Knowledge Management, pages 1043-1052, 2008. [10] J. Pitkow, H. Sch¨utze, T. Cass, R. Cooley, D. Turnbull, A. Edmonds, E. Adar, and T. Breuel. Personalized search. Commun. ACM, 45(9):50–55, 2002. [11] J. Seo, and W. B. Croft. Blog site search using resource selection. In Proc. of the ACM Conf. on Information and Knowledge Management, pages 1053-1062, 2008. [12] K. Sugiyama, K. Hatano, and M. Yoshikawa. Adaptive web search based on user profile constructed without any effort from users. In Proc. of ACM Conf. on World Wide Web, pages 675–684, 2004. [13] D. Shen, J. Sun, Q. Yang, and Z. Chen. Latent friend mining from blog data. In Proc. of the IEEE Conf. on Data Mining, pages 552-561,2006. [14] J. Teevan, S. T. Dumais, and E. Horvitz. Personalizing search via automated analysis of interests and activities. In Proc. of the ACM SIGIR Conf. on Research and Development in Information Retrieval, pages 449–456,2005.. [15] W. Zhang, C. Yu, and W. Meng. Opinion Retrieval from blogs. In Proc. of the ACM Conf. on Information and Knowledge Management, pages 831-840, 2007.
描述:	碩士國立政治大學資訊科學學系 96753018 98
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0096753018
資料類型:	thesis
顯示於類別:	[資訊科學系] 學位論文

文件中的檔案:

檔案	描述	大小	格式	瀏覽次數
301801.pdf		105Kb	Adobe PDF2	749	檢視/開啟
301802.pdf		135Kb	Adobe PDF2	721	檢視/開啟
301803.pdf		128Kb	Adobe PDF2	797	檢視/開啟
301804.pdf		157Kb	Adobe PDF2	775	檢視/開啟
301805.pdf		171Kb	Adobe PDF2	961	檢視/開啟
301806.pdf		1031Kb	Adobe PDF2	1168	檢視/開啟
301807.pdf		308Kb	Adobe PDF2	1958	檢視/開啟
301808.pdf		139Kb	Adobe PDF2	772	檢視/開啟
301809.pdf		117Kb	Adobe PDF2	769	檢視/開啟

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