研究生: |
陳映竹 Ying-Chu,Chen |
---|---|
論文名稱: |
大學生網路影音檢索之相關判斷研究 An Exploratory Study on University Students’ Relevance Judgment for Web Video Retrieval |
指導教授: |
卜小蝶
Pu, Hsiao-Tieh |
學位類別: |
碩士 Master |
系所名稱: |
圖書資訊學研究所 Graduate Institute of Library and Information Studies |
論文出版年: | 2011 |
畢業學年度: | 99 |
語文別: | 中文 |
論文頁數: | 157 |
中文關鍵詞: | 網路影音分享網站 、影音檢索行為 、相關判斷 、相關判斷準則 |
英文關鍵詞: | Online video sharing websites, Video searching behavior, Relevance judgement, Relevance judgement criteria |
論文種類: | 學術論文 |
相關次數: | 點閱:316 下載:17 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
隨著網路影音資源大量成長,使用者上網搜尋影音已相當普遍。在檢索過程中,使用者如何由大量且無組織的影音資源中過濾及取得所需資訊,已成為重要的研究議題。瞭解使用者相關判斷行為特性是提升檢索效益的重要基礎,本研究即嘗試以大學生使用者為對象,分析其網路影音之相關判斷準則、及這些準則在檢索前後之變化;同時,本研究也初步探討人口背景及檢索任務類型對其網路影音相關判斷之影響。
本研究採實驗、訪談、問卷及觀察等方法。研究對象以經常上網搜尋網路影音的大學生為主,計有16名來自不同學科背景大學生參與本研究。為避免系統因素影響,實驗平台以目前網路上最主要之影音搜尋網站Google Video為範疇,實驗中,本研究依照網路影音特性之文字資訊、影像資訊及隱性資訊提供三種類型、複雜度高低不同之六項指定檢索任務,讓受試者不限時間內完成任務,本研究以螢幕記錄軟體記錄其檢索過程,並在旁觀察。同時,受試者在檢索前後各填寫一份影音相關判斷準則評估表,檢索後,研究者再進行個別訪談。
研究結果顯示:(1)就影音相關判斷準則中,受試者的前三項準則為「主題性」、「物件/事件」及「動作」;(2)就相關判斷線索而言,在瀏覽檢索結果時,受試者多以系統所提供之文字訊息進行相關判斷;而在實際觀賞影音時,則傾向以視覺型線索如影音中的物件、動作等來進行相關判斷;此外,當有多個目標影音可供選擇時,受試者則會以熟悉度、適用性等隱性之相關判斷準則作為選擇的依據;(3)就人口特質與影音相關判斷之關聯,本研究中男性和女性受試者所重視之相關判斷準則略有不同;而學科背景則與影音相關判斷無明顯關聯;此外,接觸網路影音搜尋網站的使用頻率越高,其影音相關判斷及檢索效率結果較佳。
根據上述,本研究針對系統及使用者層面有以下建議:就網路影音搜尋網站功能之改善,系統介面可將進階功能更明顯呈現,以利使用,特別是影音排序過濾功能;同時,系統也可增加更多元的文字資訊以利使用者進行相關判斷,例如:影音內的物件、事件標籤。另一方面,使用者之影音資訊素養,也是值得重視的議題,例如受試者會忽略系統進階功能可設定搜尋影片時間範圍及依照時間將影片排序,而須逐一檢視系統提供之時間訊息;藉由對影音資源特性與影音檢索系統的基本認識、及搜尋技巧的培養與提升,將有助使用者更快速搜尋到所需影音及後續之利用。
The internet has already leaped for the second media only next to television, now the online video sharing website is one of the potential services on the internet. For promoting the efficiency of the online video search, it’s important to understand user's general video using and searching behavior. To be faced with the large number of online videos, this is a worthy issue to understand people how to judge the relevance of their needs and video searching results, and the study of video relevance judgements may help online video websites improve their searching systems.
This study attempt to online video website undergraduates’ relevance judgement criteria of choosing videos, observe users’ relevance judgements’ changes before and after accept tasks of different types. And analyze population background and the type of the tasks how to influence people’s relevance judgment criteria.
The research methods include questionnaire, experiments and interviews. This study has 16 undergraduate online video website users from different subjects. The questionnaire analyzes users’ background and their online video website using habit. The experiment tends to observe users’ video search process and their selections of relevant judgment criteria. The interview in order to understand user’s thinking when doing the video searching tasks.
The results indicate that: (1) the most important relevance judgment criteria of online video are “Topicality”.”Objects/events” and “Motion”; (2) during the video search results browing stage, people usually judge videos by the textual messages shown on online video websites, it’s similar to the situations with the general textual retrieval. When users on the video watching stage, they judge videos by audiovisual relevance criteria like objects, events or motion. When there are multiple target video choices, people places “familiarity”, “appropriateness” or other implicit criteria as selections. Also found that the stage of watching video is the most important stage for people determine ideal video; (3) Population characteristic makes influence to people’s online video relevance judgement. From this study found that men and women can be related to attention to the different criteria, and vsers’ subjects have no influence to their judgement results. However, the frequency of using online video websites takes effect of judgement, the higher frequency of users use video websites, the better searching results they could get.
Finally, the study provides some suggestions on improving online video sharing websites systems, for examples: show more obvious of advanced search features or Increase more rigorous textual information for the user to make judgments. Users also need to enhance information literacy and searching skills, which may help they do better in searching videos.
一、 中文部分
卜小蝶 (2007)。使用者導向之網路資源組織與檢索。台北市:文華。
小地方 (2007)。PPStream。上網日期:2009年12月11日。網址:http://unkb.com/category/network-multimedia/p2p-multimedia。
王盈智 (2008)。國小高年級學童之網路資訊相關判斷研究。國立臺灣師範大學圖書資訊學研究所碩士論文,未出版,台北市。
王駿發 (2007)。多媒體影音檢索系統。科學發展月刊,411,6-13。
吳奕德 (2007). YouTube使用因素與使用者行為之研究。南台科技大學企業管理研究所碩士論文,未出版,台南縣。
岳修平 (2008)。Web2.0影音分享平台之學習應用探討,臺灣圖書館管理季刊,4,9-21。
邱錫塘 (2004)。淺談影音壓縮及串流技術對網路教學應用,網路社會學通訊期刊,43。
高妮霠(2006)。Web 2.0應用服務之使用與滿足研究-以線上影音分享網站為例。 臺灣科技大學企業管理系碩士論文,台北市。
陳昭珍 (2004)。國內外影音資料數位典藏現況與趨勢暨我國影音資訊平台建置相關規範研究,發表於「國家影音產業資料庫數位典藏」,95-97。
陳慧珍 (2006)。網路圖像使用者相關判斷之研究。國立臺灣師範大學圖書資訊學研究所碩士論文,未出版,台北市。
創市際 (2009)。2009年5月創市際線上影音篇。上網日期:2009年8月1日。網址: http://www.insightxplorer.com/specialtopic/2009_5_video.htm。
創市際ARO觀察 (2009)。線上影音網站使用狀況。上網日期:2009年11月30日。網址: http://news.ixresearch.com/?p=541。
黃慕萱 (1996)。資訊檢索中『相關』概念之研究。台北市:台灣學生書局。
黃鋭海 (2009)。免費網路影音資源將減少 BT下載將成歷史,南方都市報。上網日期:2009年12月11日。網址:http://dailynews.sina.com/bg/fin/chinamkt/sinacn/20091209/0108945246.html
楊佩樺 (2008)。網路影音分享平台之使用行為調查研究。國立臺灣師範大學圖書資訊學研究所碩士論文,未出版,台北市。
楊明暐 (2009)。孩子們找資料 愛上YouTube,中國時報,上網日期:2009年12月11日。網址:http://www.nccwatch.org.tw/news/20090119/30480。
游森期(2001)。大學生網路使用行為、網路成癮及其相關因素之研究。國立彰化師範大學教育研究所碩士論文。
經濟部工業局,財團法人資訊工業策進會 (2003)。2003台灣數位內容產業年鑑 經濟部工業局。
葉建伸 (2007)。影音網站服務品質與滿意度之研究-以YouTube及無名小站為例。中國文化大學企業管理系碩士論文,未出版,台北市。
維基百科編者 (2009)。Flash Video. 上網日期:2009年12月24日。網址: http://zh.wikipedia.org/w/index.php?title=Flash_Video&oldid=11676387。
劉威麟 (2006)。YouTube成功關鍵:堅持原味,誤打誤撞。上網日期:2009年12月11日。網址:http://mr6.cc/?p=459。
數位典藏計畫後設資料工作組 (2008)。數位典藏與數位學習國家型計畫參考規範,上網日期:2009年12月24日。網址:http://metadata.teldap.tw/standard/standard-frame.html。
數位時代 (2008)。影音分享網站「剩者為王」!。上網日期:2009年10月23日。網址:http://www.bnext.com.tw/article/view/cid/0/id/9159。
鄭小祺 (2007)。圖書資訊人員之網路資源檢索技巧研究。國立臺灣師範大學圖書資訊學研究所碩士論文,未出版,台北市。
二、 英文部分
André, P., schraefel, m. c., Teevan, J. and Dumais, S. T. (2009). Discovery Is Never By Chance: Designing for (Un)Serendipity. . Paper presented at the ACM Creativity & Cognition 2009.
Armitage, L. H., & Enser, P. G. B. (1997). Analysis of user need in image archives. Journal of Information Science, 23(4), 287-299.
Barry, C. L. (1994). User-defined relevance criteria: An exploratory study. Journal of American Society for Information Science, 45(3), pp. 149–159.
Belkin, N. J., Oddy, R.N. and Brooks, H.M (1982). ASK for information retrieval. Journal of Documentation, 38(2), pp. 145–164.
Borlund, P. (2003). The concept of relevance in IR. Journal of the American Society for Information Science and Technology, 54(10), 913-925.
Brooks, H. M., & Belkin, N. J. (1983). Using discourse analysis for the design of information retrieval interaction mechanisms. SIGIR Forum, 17(4), 31-47.
Bystrom, & Jarvelin (1995). Task complexity affects information seeking and use. Information Processing & Management, 31(2), pp. 191-213.
Catledge, L. D., & Pitkow, J. E. (1995). Characterizing browsing strategies in the World-Wide web. Computer Networks and ISDN Systems, 27(6), 1065-1073.
Chen.Z, X., Y. (2005). User-Oriented Relevance Judgment: A Conceptual Model. Paper presented at the 38th Hawaii Conference on System Sciences.,
Christel, M. G., Hauptmann, A. G., Wactlar, H. D., & Ng, T. D. (2002). Collages as dynamic summaries for news video. Paper presented at the Proceedings of the tenth ACM international conference on Multimedia.
Choo, C.W., Detlor, B., & Turnbull, D. (2000). Information seeking on the web: an integrated model of browsing and searching. First Monday, 5(2). Retrieved Jan. 7, 2008, from: http://firstmonday.org/issues/issue5_2/choo/index.htm.
Cove, J. F. and Walsh (1998), Online Text Retrieval via Browsing, Information Processing and Management, Vol. 24, No. 1, 31-37.
Cosijn, E., & Ingwersen, P. (2000). Dimensions of relevance. Inf. Process. Manage., 36(4), 533-550.
Cuadra, C. A., Katter, R.V., Holmes, E.H. & Wallace, E.M. (1967). Experimental studies of relevance judgments: Final report (Vols. 1–3). Santa Monica, CA: System Development Corporation.
Cunningham, S. J., & Nichols, D. M. (2008). Finding video on the web. [Working Paper]. Hamilton, New Zealand: University of Waikato, Department of Computer Science.
Cunningham, S. J., & Nichols, D. M. (2008). How people find videos. Paper presented at the Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries.
Enser, P. (2008). The evolution of visual information retrieval. Journal of Information Science 2008, 34(4), 531-546.
Gary, G. & Sam, B.(2007). Tagging video: conventions and strategies of the YouTube community. Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries, Vancouver, BC, Canada.
Gill, P., Arlitt, M., Li, Z., & Mahanti, A. (2007). Youtube traffic characterization: a view from the edge. Paper presented at the Proceedings of the 7th ACM SIGCOMM conference on Internet measurement.
Halvey, M. J., & Keane, M. T. (2007). Analysis of online video search and sharing. Paper presented at the Proceedings of the eighteenth conference on Hypertext and hypermedia.
Hansen, K. B. P. (2005). Conceptual framework for tasks in information studies. Journal of the American Society for Information Science and Technology, 56(10), 1050-1061.
Hsieh-Yee, I. (1998). Search tactics of Web users in searching for texts, graphics, known items and subjects: A search simulation study. Reference Librarian, 60, 61-85.
Hsieh-Yee, I. (2001). Research on web search behavior. Library and Information Science Research, 23(2), 167-185.
Kim, S., & Soergel, D. (2005). Selecting and measuring task characteristics as independent variables. Proceedings of the American Society for Information Science and Technology, 42(1), NA.
Kim, S., & Oh, S. (2009). Users' relevance criteria for evaluating answers in a social Q&A site. Journal of the American Society for Information Science and Technology, 60(4), 716-727.
Li, Y., & Belkin, N. J. (2008). A faceted approach to conceptualizing tasks in information seeking. Inf. Process. Manage., 44(6), 1822-1837.
Liu, D., & Chen, T. (2009). Video retrieval based on object discovery. Computer Vision and Image Understanding, 113(3), 397-404.
MacMullin, S. D., & Taylor, R. S. (1984). Problem dimensions and information traits. The Information Society, 3, pp 91-111.
MacMullin, S. D., & Taylor, R. S. (1984). Problem dimensions and information traits. The Information Society, 3, pp. 91-111.
Marchionini, G. (2006). Human performance measures for video retrieval. Paper presented at the Proceedings of the 8th ACM international workshop on Multimedia information retrieval.
Markkula, M., & Sormunen, E. (2000). End-User Searching Challenges Indexing Practices inthe Digital Newspaper Photo Archive. Inf. Retr., 1(4), 259-285.
Mizzaro, S. (1997). Relevance: The whole history. Journal of the American Society for Information Science, 48(9), 810-832.
Mongy, S., Bouali, F., & Djeraba, C. (2005). Analyzing user's behavior on a video database. Paper presented at the Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data.
Petrelli, D. (2008). On the role of user-centred evaluation in the advancement of interactive information retrieval. Information Processing & Management, 44(1), 22-38.
Pickowicz, M. (2008). A Better Understanding of College Students’ YouTube Behaviors.
Rees, A. M., & Schultz, D.G. (1967). A field experimental approach to the study of relevance assessments in relation to document searching (Vols.1–2). Cleveland, OH: Western Reserve University, School of Library Science, Center for Documentation and Communication Research.
Ren, F., & Bracewell, D. B. (2009). Advanced Information Retrieval. Electronic Notes in Theoretical Computer Science, 225, 303-317.
Saracevic, T. (1975). RELEVANCE: A Review of and a Framework for the Thinking on the Notion in Information Science. Journal of the American Society for Information Science, 26(6), pp. 321-343.
Saracevic, T. (1995). Evaluation of evaluation in information retrieval. Paper presented at the Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval.
Saracevic, T. (1997). Relevance: a review of and a framework for the thinking on the notion in information science Readings in information retrieval (pp. 143-165): Morgan Kaufmann Publishers Inc.
Saracevic, T. (2007a). Relevance: A review of the literature and a framework for thinking on the notion in information science. Part II: nature and manifestations of relevance. J. Am. Soc. Inf. Sci. Technol., 58(13), 1915-1933.
Saracevic, T. (2007b). Relevance: A review of the literature and a framework for thinking on the notion in information science. Part III: Behavior and effects of relevance. J. Am. Soc. Inf. Sci. Technol., 58(13), 2126-2144.
Schamber, L. (1994). Relevance and information behavior. Annual Review of Information Science and Technology, 29, pp 3-48.
Schamber, L., & Bateman, J. (1996). User criteria in relevance evaluation: Toward development of a measurement scale. Proceedings of the American Society for Information Science Annual Meeting, 33, pp. 218-225.
Seo, Y.-W., & Zhang, B.-T. (2000). Learning user's preferences by analyzing Web-browsing behaviors. Paper presented at the Proceedings of the fourth international conference on Autonomous agents.
Stats, I. W. (2009). World Internet Usage Statistics News and World Population Stats Retrieved 10/2, 2009, from http://www.internetworldstats.com/stats.htm
Taylor, R. S. (1968). Question-negotiation and information seeking in libraries. College & Research Libraries, 29(3), 178-194.
Tjondronegoro, D., & Spink, A. (2008). Web search engine multimedia functionality. Information Processing & Management, 44(1), 340-357.
Tjondronegoro, D., Spink, A., & Jansen, B. J. (2009). A study and comparison of multimedia Web searching: 1997-2006. Journal of the American Society for Information Science and Technology, 60(9), 1756-1768.
Vickery, B. C. (1959). The structure of information retrieval systems. Proceedings of the International Conference on Scientific Information, 2, 1275-1290.
Vickery, B. C. (1959). Subject analysis for information retrieval. Proceedings of the International Conference on Scientific Information, Vol. 2(National Academy of Sciences, Washington, DC), pp. 855-865.
Wilson, T. D. (2000). Human information behavior. Information Science, 3(2), pp. 49-55.
Xie, I. (2009). Dimensions of tasks: influences on information-seeking and retrieving process. Journal of Documentation, 65(3), pp. 339-366.
Xu, C., Dale, C., & Jiangchuan, L. (2008). Statistics and Social Network of YouTube Videos. Paper presented at the Quality of Service, 2008. IWQoS 2008. 16th International Workshop on.
Xu, Y. (2007). Relevance judgment in epistemic and hedonic information searches. Journal of the American Society for Information Science and Technology, 58(2), 179-189.
Yang, M. (2005). An exploration of users' video relevance criteria. Unpublished OCLC Number: 65337934, 2005.
Yang, M., Wildemuth, B. M., & Marchionini, G. (2004). The relative effectiveness of concept-based versus content-based video retrieval. Paper presented at the Proceedings of the 12th annual ACM international conference on Multimedia.
Yunjie (Calvin) Xu, Z. C. (2006). Relevance Judgment - What Do Information Users Consider beyond Topicality? . journal of the American Society for Information Science and Technology, 57(7), pp. 961-973.
Zaichkowsky, J. L. (1985). Measuring the Involvement Construct. Journal of Consumer Research, 12(3), 341.
Zanetti, S., Zelnik-Manor, L., & Perona, P. (2008). A walk through the web's video clips. Paper presented at the Computer Vision and Pattern Recognition Workshops, 2008. CVPRW '08. IEEE Computer Society Conference on.