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Found 4 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.
Multi-channel Autobidding with Budget and ROI Constraints
In light of this finding, under a bandit feedback setting that mimics real-world scenarios where advertisers have limited information on ad auctions in each channels and how channels procure ads, we present an efficient learning algorithm that produces per-channel budgets whose resulting conversion approximates that of the global optimal problem.
Decision Trees for Decision-Making under the Predict-then-Optimize Framework
We consider the use of decision trees for decision-making problems under the predict-then-optimize framework.
Incentive-aware Contextual Pricing with Non-parametric Market Noise
We show that this design allows the seller to control the number of periods in which buyers significantly corrupt their bids.
