Improving Post-Click User Engagement on Native Ads via Survival Analysis

By Nicola Barbieri, Fabrizio Silvestri, Mounia Lalmas. 2016

66 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Chandan K. Reddy
2. Yue Shi
3. Qingyun Wu
4. Miriam Redi
5. Yan Li
6. Hongning Wang
7. Liangjie Hong
8. Ping Wang

In Statistical and Nonlinear Physics

1. Yue Shi
2. Qingyun Wu
3. Hongning Wang
4. Liangjie Hong

Citations in the corpus

• On the Value of Wikipedia as a Gateway to the Web (2021, background): “In computational advertising, existing works have tried to improve ad serving based on target engagement metrics [4, 35], or to directly predict ad click-through rates [17].”
• Machine Learning for Survival Analysis: A Survey (2017, background): “Nowadays, many free web services, including online news portals, search engines and social networks present users with advertisements [Barbieri et al. 2016].”
• Quantifying Engagement with Citations on Wikipedia (2020, unknown): “, in computational advertising [6, 70], social media [5, 10, 22], or information retrieval [24, 55].”
• Returning is Believing: Optimizing Long-term User Engagement in Recommender Systems (2017, methodology): “[5] used survival analysis to estimate users’ post-click engagement on native ads by predicting the dwell time on the corresponding ad landing pages.”
• Machine Learning for Survival Analysis (2019, background): “Nowadays, many free web services, including online news portals, search engines, and social networks present users with advertisements (Barbieri et al. 2016).”