Click-through rate prediction in online advertising: A literature review

Y Yang, P Zhai - Information Processing & Management, 2022 - Elsevier
Predicting the probability that a user will click on a specific advertisement has been a
prevalent issue in online advertising, attracting much research attention in the past decades …

Unbiased sequential recommendation with latent confounders

Z Wang, S Shen, Z Wang, B Chen, X Chen… - Proceedings of the ACM …, 2022 - dl.acm.org
Sequential recommendation holds the promise of understanding user preference by
capturing successive behavior correlations. Existing research focus on designing different …

Fairness issues, current approaches, and challenges in machine learning models

TD Jui, P Rivas - International Journal of Machine Learning and …, 2024 - Springer
With the increasing influence of machine learning algorithms in decision-making processes,
concerns about fairness have gained significant attention. This area now offers significant …

On the opportunity of causal learning in recommendation systems: Foundation, estimation, prediction and challenges

P Wu, H Li, Y Deng, W Hu, Q Dai, Z Dong, J Sun… - arXiv preprint arXiv …, 2022 - arxiv.org
Recently, recommender system (RS) based on causal inference has gained much attention
in the industrial community, as well as the states of the art performance in many prediction …

Who should be given incentives? counterfactual optimal treatment regimes learning for recommendation

H Li, C Zheng, P Wu, K Kuang, Y Liu… - Proceedings of the 29th …, 2023 - dl.acm.org
Effective personalized incentives can improve user experience and increase platform
revenue, resulting in a win-win situation between users and e-commerce companies …

Improving ad click prediction by considering non-displayed events

B Yuan, JY Hsia, MY Yang, H Zhu, CY Chang… - Proceedings of the 28th …, 2019 - dl.acm.org
Click-through rate (CTR) prediction is the core problem of building advertising systems. Most
existing state-of-the-art approaches model CTR prediction as binary classification problems …

Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation

H Zhao, L Zhang, J Xu, G Cai, Z Dong… - Proceedings of the 17th …, 2023 - dl.acm.org
In the video recommendation, watch time is commonly adopted as an indicator of user
interest. However, watch time is not only influenced by the matching of users' interests but …

Pareto invariant representation learning for multimedia recommendation

S Huang, H Li, Q Li, C Zheng, L Liu - Proceedings of the 31st ACM …, 2023 - dl.acm.org
Multimedia recommendation involves personalized ranking tasks, where multimedia content
is usually represented using a generic encoder. However, these generic representations …

A brief history of recommender systems

Z Dong, Z Wang, J Xu, R Tang, J Wen - arXiv preprint arXiv:2209.01860, 2022 - arxiv.org
Soon after the invention of the Internet, the recommender system emerged and related
technologies have been extensively studied and applied by both academia and industry …

Counteracting Duration Bias in Video Recommendation via Counterfactual Watch Time

H Zhao, G Cai, J Zhu, Z Dong, J Xu… - Proceedings of the 30th …, 2024 - dl.acm.org
In video recommendation, an ongoing effort is to satisfy users' personalized information
needs by leveraging their logged watch time. However, watch time prediction suffers from …