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Found 8 papers, 2 papers with code
A State Transition Model for Mobile Notifications via Survival Analysis
In particular, we provide an online use case on notification delivery time optimization to show how we make better decisions, drive more user engagement, and provide more value to users.
Generalized Causal Tree for Uplift Modeling
In this paper, we propose a generalization of tree-based approaches to tackle multiple discrete and continuous-valued treatments.
Feedback Shaping: A Modeling Approach to Nurture Content Creation
In this work, we propose a modeling approach to predict how feedback from content consumers incentivizes creators.
Measuring Long-term Impact of Ads on LinkedIn Feed
In this paper, we design experiments to understand how members' behavior evolve over time given different ads experiences.
Personalized Treatment Selection using Causal Heterogeneity
In this work, we develop a framework for personalization through (i) estimation of heterogeneous treatment effect at either a cohort or member-level, followed by (ii) selection of optimal treatment variants for cohorts (or members) obtained through (deterministic or stochastic) constrained optimization.
Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences
We consider the problem of solving a large-scale Quadratically Constrained Quadratic Program.
Constrained Multi-Slot Optimization for Ranking Recommendations
Ranking items to be recommended to users is one of the main problems in large scale social media applications.
Large scale multi-objective optimization: Theoretical and practical challenges
Multi-objective optimization (MOO) is a well-studied problem for several important recommendation problems.
