When large online platforms test a new strategy to implement with their user traffic, the phenomenon of diminishing marginal effects may arise. For example, when a strategy is implemented on 100% of the user traffic, the expected per-user effect can be lower compared to the expected per-user effect when a strategy is implemented on 10% of the user traffic, potentially due to limits of overall budget, resource, attention or content involved with that strategy. This diminishing marginal effect phenomenon brings an additional delicacy to online sequential experiments. In particular, for the classical goal of achieving the largest expected reward, the optimal decision may no longer be assigning 100% traffic to one strategy, but instead a mixture of strategies. We deliver two tasks for online sequential experiments in presence of diminishing marginal effect: (1) Adaptively identify the optimal traffic allocation to maximize the expected cumulative reward and (2) Construct valid central limit theorem (which is critically needed for A/B tests in online platforms) to perform reliable statistical inference for the expected reward under the optimal traffic allocation that is a priori unknown. We show that classical algorithms can fail to deliver the second task, especially because the statistical inference task presents its own difficulty. We develop a new online algorithm that leverages an additional smoothness condition on how the marginal effects change to achieve both tasks. We prove that this algorithm obtains the best achievable expected cumulative reward. Further, crucially for online platforms' need to do trustworthy statistical inference, the algorithm is proved to enjoy a valid central limit theorem. The theoretical findings are illustrated through numerical experiments.

E-commerce retailers increasingly offer diverse fulfillment options (e.g., in-store pickup, same-day delivery from store, standard shipping from fulfillment centers and marketplace sellers), creating a need for search systems that understand and cater to individual customer preferences. This paper addresses the challenge of incorporating fulfillment intent into product search ranking. We propose a model that predicts customer fulfillment intent based on the search query, past user interactions, and other contextual information. A fulfillment match signal is introduced to quantify the alignment between a product's available fulfillment methods and the predicted customer intent. Integrating this signal into the ranking process ensures that search results prioritize products matching the user's preferred fulfillment type. Offline and online experiments demonstrate the efficacy of our approach and highlight the importance of fulfillment-aware ranking in omnichannel e-commerce.

We propose gradient-based simulation-optimization algorithms to optimize systems that have complicated stochastic structure. The presence of complicated stochastic structure, such as the involvement of infinite-dimensional continuous-time stochastic processes, may cause the exact simulation of the system to be costly or even impossible. On the other hand, for a complicated system, one can sometimes construct a sequence of approximations at different resolutions, where the sequence has finer and finer approximation resolution but higher and higher cost to simulate. With the goal of optimizing the complicated system, we propose algorithms that strategically use the approximations with increasing resolution and higher simulation cost to construct stochastic gradients and perform gradient search in the decision space. To accommodate scenarios where approximations cause discontinuities and lead path-wise gradient estimators to have an uncontrollable bias, stochastic gradients for the proposed algorithms are constructed through finite difference. As a theory support, we prove algorithm convergence, convergence rate, and optimality of algorithm design under the assumption that the objective function for the complicated system is strongly convex, whereas no such assumptions are imposed on the approximations of the complicated system. We then present a multilevel version of the proposed algorithms to further improve convergence rates, when in addition the sequence of approximations can be naturally coupled. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1279 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0289 ) at ( http://dx.doi.org/10.5281/zenodo.7485443 ).

Breast density is widely adopted to reflect the likelihood of early breast cancer development. Existing methods of mammographic density classification either require steps of manual operations or achieve only moderate classification accuracy due to the limited model capacity. In this study, we present a radiomics approach based on dilated and attention-guided residual learning for the task of mammographic density classification. The proposed method was instantiated with two datasets, one clinical dataset and one publicly available dataset, and classification accuracies of 88.7 and 70.0 percent were obtained, respectively. Although the classification accuracy of the public dataset was lower than the clinical dataset, which was very likely related to the dataset size, our proposed model still achieved a better performance than the naive residual networks and several recently published deep learning-based approaches. Furthermore, we designed a multi-stream network architecture specifically targeting at analyzing the multi-view mammograms. Utilizing the clinical dataset, we validated that multi-view inputs were beneficial to the breast density classification task with an increase of at least 2.0 percent in accuracy and the different views lead to different model classification capacities. Our method has a great potential to be further developed and applied in computer-aided diagnosis systems. Our code is available at https://github.com/lich0031/Mammographic_Density_Classification.