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

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...

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Veröffentlicht in:Data Science and Engineering Jg. 5; H. 1; S. 12 - 26
Hauptverfasser: Gharibshah, Zhabiz, Zhu, Xingquan, Hainline, Arthur, Conway, Michael
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2020
Springer
SpringerOpen
Schlagworte:
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
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Zusammenfassung: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.
ISSN:2364-1185
2364-1541
DOI:10.1007/s41019-019-00115-y