應用文氏圖文字探勘與眼球追蹤技術於廣告推薦上

Applying the Venn Diagram Based Text-Mining and Eye-Tracking Technology to Advertising Recommendation

賴群宇(Qun-Yu Lai)

指導教授 ： 戴榮賦

國立暨南國際大學/管理學院/資訊管理學系/碩士(2016年)

摘要

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。

關鍵字

文字探勘 ； 眼球追蹤 ； 個人化廣告 ； 偏好預測

並列摘要

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.

並列關鍵字

Text Mining ； Eye tracking ； Personalized ads ； Preference predict

參考文獻

參考文獻

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.