Deep Learning for User Interest and Response Prediction in Online Display Advertising

• Published in: Data Science and Engineering Vol. 5; no. 1; pp. 12 - 26
• Main Authors: Gharibshah, Zhabiz, Zhu, Xingquan, Hainline, Arthur, Conway, Michael
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2020; Springer; SpringerOpen
• Subjects: Advertising campaigns; Advertising executives; Algorithm Analysis and Problem Complexity; Analysis; Artificial Intelligence; Campaign; Chemistry and Earth Sciences; Click prediction; Computer Science; Data Mining and Knowledge Discovery; Database Management; Deep learning; Display advertising; Email marketing; LSTM network; Machine learning; Physics; Statistics for Engineering; Systems and Data Security; New York; Taiwan; Deep learning; Click prediction; Display advertising; Campaign; LSTM network

User interest and behavior modeling is a critical step in online digital advertising. On the one hand, user interests directly impact their response and actions to the displayed advertisement (Ad). On the other hand, user interests can further help determine the probability of an Ad viewer becoming...