Deep Learning for Online Display Advertising User Clicks and Interests Prediction

Gharibshah, Zhabiz; Zhu, Xingquan; Hainline, Arthur; Conway, Michael

doi:10.1007/978-3-030-26072-9_14

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…We generalize these two problems as a binary classification task (for user click prediction) and a multi-class classification task (for user interest prediction). More specifically, we collect users' page visits as a temporal sequence and train deep LSTM (long short-term memory) networks to make predictions [7]. A unique strength of the proposed model is that it considers users' temporal information to model their response and interests.…”

Section: Challenges and Solutionsmentioning

confidence: 99%

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

Gharibshah

Zhu

Hainline³

et al. 2020

Data Sci. Eng.

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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.

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Section: Challenges and Solutionsmentioning

confidence: 99%

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

Gharibshah

Zhu

Hainline³

et al. 2020

Data Sci. Eng.

Self Cite

View full text Add to dashboard Cite

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Personalized Advertising Computational Techniques: A Systematic Literature Review, Findings, and a Design Framework

Viktoratos

Tsadiras

2021

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This work conducts a systematic literature review about the domain of personalized advertisement, and more specifically, about the techniques that are used for this purpose. State-of-the-art publications and techniques are presented in detail, and the relationship of this domain with other related domains such as artificial intelligence (AI), semantic web, etc., is investigated. Important issues such as (a) business data utilization in personalized advertisement models, (b) the cold start problem in the domain, (c) advertisement visualization issues, (d) psychological factors in the personalization models, (e) the lack of rich datasets, and (f) user privacy are highlighted and are pinpointed to help and inspire researchers for future work. Finally, a design framework for personalized advertisement systems has been designed based on these findings.

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Deep Learning for Online Display Advertising User Clicks and Interests Prediction

Cited by 2 publications

References 9 publications

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

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

Personalized Advertising Computational Techniques: A Systematic Literature Review, Findings, and a Design Framework

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