12 citations · 54 across the 11 of their papers we have counts for
11 papers
A State Transition Model for Mobile Notifications via Survival Analysis
Yiping Yuan, Jing Zhang, Shaunak Chatterjee +2
Mobile notifications have become a major communication channel for social networking services to keep users informed and engaged. As more mobile applications push notifications to…
Generalized Causal Tree for Uplift Modeling
Preetam Nandy, Xiufan Yu, Wanjun Liu +3
Uplift modeling is crucial in various applications ranging from marketing and policy-making to personalized recommendations. The main objective is to learn optimal treatment alloca…
Feedback Shaping: A Modeling Approach to Nurture Content Creation
Ye Tu, Chun Lo, Yiping Yuan +1
Social media platforms bring together content creators and content consumers through recommender systems like newsfeed. The focus of such recommender systems has thus far been prim…
A/B Testing for Recommender Systems in a Two-sided Marketplace
Preetam Nandy, Divya Venugopalan, Chun Lo +1
Two-sided marketplaces are standard business models of many online platforms (e.g., Amazon, Facebook, LinkedIn), wherein the platforms have consumers, buyers or content viewers on…
Measuring Long-term Impact of Ads on LinkedIn Feed
Jinyun Yan, Birjodh Tiwana, Souvik Ghosh +2
Organic updates (from a member's network) and sponsored updates (or ads, from advertisers) together form the newsfeed on LinkedIn. The newsfeed, the default homepage for members, a…
Personalized Treatment Selection using Causal Heterogeneity
Ye Tu, Kinjal Basu, Cyrus DiCiccio +4
Randomized experimentation (also known as A/B testing or bucket testing) is widely used in the internet industry to measure the metric impact obtained by different treatment varian…