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
• ISSN: 2364-1185; 2364-1541
• DOI: 10.1007/s41019-019-00115-y

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 a buying customer. To date, existing methods for Ad click prediction, or click-through rate prediction, mainly consider representing users as a static feature set and train machine learning classifiers to predict clicks. Such approaches do not consider temporal variance and changes in user behaviors, and solely rely on given features for learning. In this paper, we propose two deep learning-based frameworks, LSTM cp and LSTM ip , for user click prediction and user interest modeling. Our goal is to accurately predict (1) the probability of a user clicking on an Ad and (2) the probability of a user clicking a specific type of Ad campaign. To achieve the goal, we collect page information displayed to the users as a temporal sequence and use long short-term memory (LSTM) network to learn features that represents user interests as latent features. Experiments and comparisons on real-world data show that, compared to existing static set-based approaches, considering sequences and temporal variance of user requests results in improvements in user Ad response prediction and campaign specific user Ad click prediction.