TL;DR
A novel distributionally robust model for the Operator-oriented Point-of-interest Recommendation Problem (OO-POIRP) with demand uncertainty is proposed, aiming to assist operators in minimizing cooperation cost, ticket booking cost, promotion cost, and transportation cost.
. Multi-armed bandit (MAB) algorithms are commonly used in sequential decision tasks such as online recommendation and advertising. In real-world systems, algorithms often have limited time and data to learn from, and poor decisions can be costly. From a finite-horizon perspective, this paper presents a mechanism-level...
This paper forms the resulting $\ell_0$-constrained empirical newsvendor problem with $\ell_2$-regularization, establishes its computational hardness, and develops a mixed-integer second-order cone programming reformulation that strengthens the standard Big-$M$ formulation.
Most existing studies on distributionally robust optimization (DRO) with a decision-dependent ambiguity set focus on the computational and theoretical challenges posed by this class of problems. In this paper, we adopt a combined modeling and computational perspective to understand the trade-offs between modeling fidel...
Historically, recommendation systems have focused on maximizing precision by treating user preference as a static, predictable target. However, this approach ignores both the inherent randomness of human behavior and the model’s own varying levels of confidence. To compensate, many industrial systems rely on "reserved...
The algorithm is parameterized so as to address various stochastic formulations spanning from Expectation-focused to Value-at-Risk (VaR) as well as Conditional-Value-at-Risk (CVaR) as well as Conditional-Value-at-Risk (CVaR)-focused formulations.
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