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研究生:賴群宇
研究生(外文):Lai, Qun-Yu
論文名稱:應用文氏圖文字探勘與眼球追蹤技術於廣告推薦上
論文名稱(外文):Applying the Venn Diagram Based Text-Mining and Eye-Tracking Technology to Advertising Recommendation
指導教授:戴榮賦戴榮賦引用關係
指導教授(外文):Rong-Fuh Day
口試委員:陳建宏連俊瑋
口試委員(外文):Jian-Hung ChenJiunn-Woei Lian
口試日期:2016-07-28
學位類別:碩士
校院名稱:國立暨南國際大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:中文
論文頁數:30
中文關鍵詞:文字探勘眼球追蹤個人化廣告偏好預測
外文關鍵詞:Text MiningEye trackingPersonalized adsPreference predict
相關次數:
  • 被引用被引用:2
  • 點閱點閱:423
  • 評分評分:
  • 下載下載:99
  • 收藏至我的研究室書目清單書目收藏:1
網路廣告常以鮮明圖文或彈跳式視窗來吸引使用者注意,較少探索用戶在瀏覽時與系統互動所產生的資訊內容。本研究嘗試以人的自然閱讀文章行為,運用注視內容來探索使用者的潛在偏好並推薦其個人化廣告。本實驗系統將使用眼動追蹤技術,並結合文氏圖概念提出一種文字探勘方法,以文氏圖的概念,將不同集合之間的詞賦予權重值,把文字轉換為可計算的單位。本研究先驗證此方法在分類上的成效,並透過實驗來分析其預測能力。結果顯示,系統在廣告推薦預測上,高於非隨機機率的門檻值。
While online advertising usually incorporates eye-catching images or pop-ups to attract user attention, it is less likely to mine data generated by user interaction with the system when browsing. This study attempts to utilize natural human reading behavior and determine which contents user attention is focused on to investigate users’ underlying preferences and recommend targeted advertising for said users. The experiment utilizes eye tracking technology combined with Venn diagram concepts to propose a data mining method. Using Venn diagram concepts, words in different sets are assigned weighted values and are transformed into units that can be used for calculation. This study first verified the effectiveness of the method of categorization, and then analyzed its predictive ability via experimentation. Results show that, in terms of advertising recommendation predictivity, the system exceeds the value of the threshold for non-random probability.
致謝辭 I
摘要 II
Abstract III
目次 IV
圖目次 VI
表目次 VII
壹、 導論 1
一、 研究背景與動機 1
(一) 文字探勘(Text Mining) 1
(二) 廣告視盲 1
(三) 使用者回饋機制 1
(四) 眼動追蹤 2
二、 研究問題與範圍 2
(一) 驗證本研究的文字探勘演算法在分類上的成效 2
(二) 設計一套整合眼動追蹤的調適性的廣告推薦系統 2
貳、 文獻探討 3
一、 眼動追蹤文獻 3
(一) 何謂眼動追蹤 3
(二) 以眼動追蹤預測喜好 3
(三) 應用於廣告推薦 4
(四) 眼動追蹤相關詞彙 4
二、 文字探勘 5
三、 文氏圖相關文獻探討 5
四、 個人化文獻探討 6
(一) 個人化系統 6
(二) 個人化廣告 7
參、 研究方法 8
一、 實驗設計 8
(一) 蒐集資料 8
(二) 文氏圖分類加權計分法 9
(三) 分類成效 11
(四) 廣告推薦實驗系統 14
(五) 評估指標 18
二、 實驗方法 19
(一) 受測者 19
(二) 前測 19
(三) 調查問卷 19
(四) 實驗流程 20
肆、 資料分析 22
一、 不同探勘資料量對於分類上的準確率影響 22
(一) 資料探勘數量為80篇 22
(二) 資料探勘數量為120篇 22
(三) 資料探勘數量為160篇 23
二、 系統廣告推薦準確率 24
伍、 結論與建議 26
一、 研究發現 26
二、 研究限制與後續研究 26
(一) 研究限制 26
(二) 未來研究 27
參考文獻 29

圖目次
圖 1:Venn的二集合文氏圖 6
圖 2:實驗設計概念圖 8
圖 3:參考Venn所使用的橢圓四集合文氏圖 10
圖 4:權重分數示意圖 11
圖 5:分類系統概念圖 12
圖 6:系統架構圖 13
圖 7:電影分類程式 14
圖 8:刺激物及定義的興趣區塊(AOI)示意圖 16
圖 9:喜劇類橫幅廣告刺激物 16
圖 10:動作類橫幅廣告刺激物 16
圖 11:科幻類橫幅廣告刺激物 17
圖 12:懸疑類橫幅廣告刺激物 17
圖 13:系統實驗敬語 21
圖 14:系統實驗瀏覽器圖-1 21
圖 15:不同探勘文章數量對預測準確率之影響 24

表目次
表 1:權重分數表 11
表 2:實驗系統詳細功能 15
表 3:混亂矩陣(Confusion Matrix) 19
表 4:資料探勘數量為80篇之混亂矩陣 22
表 5:資料探勘數量為120篇之混亂矩陣 23
表 6:資料探勘數量為160篇之混亂矩陣 23
表 7:系統廣告推薦準確率之混淆矩陣 25


Bang, H., and Wojdynski, B. W. 2016. "Tracking Users' Visual Attention and Responses to Personalized Advertising Based on Task Cognitive Demand," Computers in Human Behavior (55), pp. 867-876.
Blom, J. O., and Monk, A. F. 2003. "Theory of Personalization of Appearance: Why Users Personalize Their Pcs and Mobile Phones," Human-Computer Interaction (18:3), pp. 193-228.
Cheng, S., Liu, X., Yan, P., Zhou, J., and Sun, S. 2010. "Adaptive User Interface of Product Recommendation Based on Eye-Tracking," Proceedings of the 2010 workshop on Eye gaze in intelligent human machine interaction: ACM, pp. 94-101.
Feldman, R., Fresko, M., Kinar, Y., Lindell, Y., Liphstat, O., Rajman, M., Schler, Y., and Zamir, O. 1998. "Text Mining at the Term Level," European Symposium on Principles of Data Mining and Knowledge Discovery: Springer, pp. 65-73.
Glaholt, M. G., Wu, M.-C., and Reingold, E. M. 2009. "Predicting Preference from Fixations," PsychNology Journal (7:2), pp. 141-158.
Hearst, M. A. 1999. "Untangling Text Data Mining," Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics: Association for Computational Linguistics, pp. 3-10.
Henderson, J. M., and Hollingworth, A. 1999. "High-Level Scene Perception," Annual review of psychology (50:1), pp. 243-271.
Hirsh, H., Basu, C., and Davison, B. D. 2000. "Learning to Personalize," Communications of the ACM (43:8), pp. 102-106.
Hotho, A., Nürnberger, A., and Paaß, G. 2005. "A Brief Survey of Text Mining," Ldv Forum, pp. 19-62.
Howard, D. J., and Kerin, R. A. 2004. "The Effects of Personalized Product Recommendations on Advertisement Response Rates: The “Try This. It Works!” Technique," Journal of consumer psychology (14:3), pp. 271-279.
Jacob, R., and Karn, K. S. 2003. "Eye Tracking in Human-Computer Interaction and Usability Research: Ready to Deliver the Promises," Mind (2:3), p. 4.
Just, M. A., and Carpenter, P. A. 1976. "Eye Fixations and Cognitive Processes," Cognitive psychology (8:4), pp. 441-480.
Kagdi, H., Yusuf, S., and Maletic, J. I. 2007. "On Using Eye Tracking in Empirical Assessment of Software Visualizations," Proceedings of the 1st ACM international workshop on Empirical assessment of software engineering languages and technologies: held in conjunction with the 22nd IEEE/ACM International Conference on Automated Software Engineering (ASE) 2007: ACM, pp. 21-22.
Kahneman, D. 1973. Attention and Effort. Citeseer.
Lapa, C. 2007. "Using Eye Tracking to Understand Banner Blindness and Improve Website Design,").
Maughan, L., Gutnikov, S., and Stevens, R. 2007. "Like More, Look More. Look More, Like More: The Evidence from Eye-Tracking," The Journal of Brand Management (14:4), pp. 335-342.
Mei, Q., and Zhai, C. 2005. "Discovering Evolutionary Theme Patterns from Text: An Exploration of Temporal Text Mining," Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining: ACM, pp. 198-207.
Mobasher, B., Cooley, R., and Srivastava, J. 2000. "Automatic Personalization Based on Web Usage Mining," Communications of the ACM (43:8), pp. 142-151.
Owens, J. W., Chaparro, B. S., and Palmer, E. M. 2011. "Text Advertising Blindness: The New Banner Blindness?," Journal of Usability Studies (6:3), pp. 172-197.
Pieters, R., and Warlop, L. 1999. "Visual Attention During Brand Choice: The Impact of Time Pressure and Task Motivation," International Journal of Research in Marketing (16:1), pp. 1-16.
Pieters, R., and Wedel, M. 2004. "Attention Capture and Transfer in Advertising: Brand, Pictorial, and Text-Size Effects," Journal of Marketing (68:2), pp. 36-50.
Poole, A., and Ball, L. J. 2006. "Eye Tracking in Hci and Usability Research," Encyclopedia of human computer interaction (1), pp. 211-219.
Qvarfordt, P., and Zhai, S. 2005. "Conversing with the User Based on Eye-Gaze Patterns," Proceedings of the SIGCHI conference on Human factors in computing systems: ACM, pp. 221-230.
Rajman, M., and Besançon, R. 1998. "Text Mining: Natural Language Techniques and Text Mining Applications," in Data Mining and Reverse Engineering. Springer, pp. 50-64.
Rayner, K. 1998. "Eye Movements in Reading and Information Processing: 20 Years of Research," Psychological bulletin (124:3), p. 372.
Rayner, K., Rotello, C. M., Stewart, A. J., Keir, J., and Duffy, S. A. 2001. "Integrating Text and Pictorial Information: Eye Movements When Looking at Print Advertisements," Journal of Experimental Psychology: Applied (7:3), p. 219.
Resnick, M., and Albert, W. 2014. "The Impact of Advertising Location and User Task on the Emergence of Banner Ad Blindness: An Eye-Tracking Study," International Journal of Human-Computer Interaction (30:3), pp. 206-219.
Riecken, D. 2000. "Personalized Views of Personalization," Communications of the ACM (43:8), pp. 26-26.
Sakagami, H., and Kamba, T. 1997. "Learning Personal Preferences on Online Newspaper Articles from User Behaviors," Computer Networks and ISDN Systems (29:8), pp. 1447-1455.
Salvucci, D. D., and Goldberg, J. H. 2000. "Identifying Fixations and Saccades in Eye-Tracking Protocols," Proceedings of the 2000 symposium on Eye tracking research & applications: ACM, pp. 71-78.
Tan, A.-H. 1999. "Text Mining: The State of the Art and the Challenges," Proceedings of the PAKDD 1999 Workshop on Knowledge Disocovery from Advanced Databases, pp. 65-70.
Venn, J. 1880. "I. On the Diagrammatic and Mechanical Representation of Propositions and Reasonings," The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science (10:59), pp. 1-18.


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