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Found 3 papers, 1 papers with code
Interpolating Item and User Fairness in Multi-Sided Recommendations
Motivated by this, we formulate a novel fair recommendation framework, called Problem (FAIR), that not only maximizes the platform's revenue, but also accommodates varying fairness considerations from the perspectives of items and users.
Non-Stationary Bandits with Auto-Regressive Temporal Dependency
Traditional multi-armed bandit (MAB) frameworks, predominantly examined under stochastic or adversarial settings, often overlook the temporal dynamics inherent in many real-world applications such as recommendation systems and online advertising.
Online Learning and Matching for Resource Allocation Problems
In this work, our goal is to recommend items to users as they arrive on a webpage sequentially, in an online manner, in order to maximize reward for a company, but also satisfy budget constraints.
