Experimental design is critical for evidence-based decision-making in healthcare, marketing, and public policy. However, designing efficient experiments across heterogeneous subgroups presents significant challenges. Existing methods often optimize for statistical power or overall sample efficiency, overlooking crucial fairness considerations across these different subgroups. To address this gap, we introduce a Fairness-Aware Contextual Track-and-Stop Design (F-CTSD) algorithm. The proposed F-CTSD algorithm provides statistical guarantees on subgroup fairness while minimizing required sample sizes. We quantify the fairness-efficiency trade-off and derive the sample complexity bound for the proposed F-CTSD algorithm under its fairness constraints. We further theoretically prove that the proposed F-CTSD algorithm consistently produces accurate treatment effect estimates even under fairness requirements, enhancing statistical reliability. Numerical experiments show that the proposed F-CTSD algorithm outperforms existing methods, achieving higher sample efficiency while reducing subgroup fairness violations by 4.95%.

Learning-to-rank (LTR) is a fundamental component of modern large-scale information retrieval (IR) systems, playing an essential role across various stages of the ranking pipeline. Recently, differentiable sorting networks have attracted increasing attention for LTR as a permutation-level learning paradigm, enabling end-to-end optimization directly on ranking structure. However, existing approaches suffer from two critical limitations: (i) permutation-matrix fidelity, i.e., the predicted soft permutation matrix may deviate from the exact hard permutation matrix required by permutation-level objectives; and (ii) uncertainty in target ordering arising from coarse or tied relevance labels, where the ground-truth order is set-valued rather than unique.

Traditional recommendation methods model a user's interest in a target item by correlating its embedding with the embeddings of items from the user's interaction history, thereby capturing implicit collaborative filtering signals. Consequently, traditional ID-based methods often encounter data sparsity problems stemming from the sparse nature of ID features. To mitigate this issue, recommendation models incorporate multimodal item information to enhance recommendation accuracy. However, existing multimodal recommendation methods typically rely on early fusion approaches, which focus primarily on combining text and image features, while neglecting the dynamic context provided by user behavior sequences. This oversight precludes the dynamic adaptation of multimodal interest representations to behavioral patterns, thereby hindering the model's ability to effectively capture user multimodal interests. Therefore, this paper proposes the Distribution-Guided Multimodal-Interest Auto-Encoder (DMAE), which achieves the cross fusion of user multimodal interest at the behavioral level. Specifically, DMAE comprises three key components: 1) Multimodal Interest Encoding Unit (MIEU), which encodes the similarity scores between the target item and historically clicked items as the corresponding representation vectors of user interest across different modalities. 2) Multimodal Interest Fusion Unit (MIFU), which dynamically adapts these interest representations through both intra- and inter-modal fusion, a process contextualized by the user's behavioral sequence to achieve a fine-grained and behavior-aware representation of interest. 3) Interest-Distribution Decoding Unit (IDDU), which employs a decoder to reconstruct the encoded user interest representations into true similarity distributions for each modality. The similarity distributions serve as a guide for model learning, aiming to retain as much multimodal information as possible. Ultimately, extensive experiments demonstrate the superiority of DMAE.

In many machine learning tasks, it is often necessary for the relationship between input and output variables to be monotonic, including both strictly monotonic and implicitly monotonic relationships. Traditional methods for maintaining monotonicity mainly rely on construction or regularization techniques, whereas this paper shows that the issue of strict monotonic probability can be viewed as a partial order between an observable revenue variable and a latent cost variable. This perspective enables us to reformulate the monotonicity challenge into modeling the latent cost variable. To tackle this, we introduce a generative network for the latent cost variable, termed the Generative Cost Model (GCM), which inherently addresses the strict monotonic problem, and propose the Implicit Generative Cost Model (IGCM) to address the implicit monotonic problem. We further validate our approach with a numerical simulation of quantile regression and conduct multiple experiments on public datasets, showing that our method significantly outperforms existing monotonic modeling techniques. The code for our experiments can be found at https://github.com/tyxaaron/GCM.

Recovering potential user preferences from user-item interaction matrices is a key challenge in recommender systems. While diffusion models can sample and reconstruct preferences from latent distributions, they often fail to capture similar users' collective preferences effectively. Additionally, latent variables degrade into pure Gaussian noise during the forward process, lowering the signal-to-noise ratio, which in turn degrades performance. To address this, we propose S-Diff, inspired by graph-based collaborative filtering, better to utilize low-frequency components in the graph spectral domain. S-Diff maps user interaction vectors into the spectral domain and parameterizes diffusion noise to align with graph frequency. As a result, this anisotropic diffusion retains significant low-frequency components, preserving a high signal-to-noise ratio. S-Diff further employs a conditional denoising network to encode user interactions, recovering true preferences from noisy data. This method achieves promising results across multiple datasets.

As e-commerce has evolved, commercial platforms accommodate various scenarios to cater to the diverse shopping preferences of users.To conserve resources, current methods utilize a unified framework to deliver personalized recommendations across various scenarios.Given the overlap of users and items in multiple scenarios, current methods typically employ shared bottom representations, capturing similarities and differences between scenarios through adaptive adjustments.However, they adjust representations adaptively after aggregating user behavior sequences.This coarse-grained approach to re-weighting the entire user sequence hampers the model's ability to model the user interest migration across different scenarios.To enhance the model's capacity to capture user interests across scenarios, we develop a ranking framework named the Scenario-Adaptive Fine-Grained Personalization Network (SFPNet), which designs a fine-grained method for multiscenario personalized recommendations.Specifically, SFPNet comprises a series of blocks, stacked sequentially.Each block initially deploys a parameter personalization unit to integrate scenario information into fundamental features at a coarse-grained level, where adjusted feature representations will serve as context information.By employing residual connection, we incorporate the context into the representation of each historical behavior, allowing for contextaware fine-grained customization of the behavior representations at the scenario-level, which supports scenario-aware user interest modeling.Ultimately, the effectiveness of our method is strongly substantiated by extensive experiments and online A/B testing.

In large-scale recommender systems, retrieving top N relevant candidates accurately with resource constrain is crucial. To evaluate the performance of such retrieval models, [email protected], the frequency of positive samples being retrieved in the top N ranking, is widely used. However, most of the conventional loss functions for retrieval models such as softmax cross-entropy and pairwise comparison methods do not directly optimize [email protected] Moreover, those conventional loss functions cannot be customized for the specific retrieval size N required by each application and thus may lead to sub-optimal performance. In this paper, we proposed the Customizable R[email protected] Optimization Loss (CROLoss), a loss function that can directly optimize the [email protected] metrics and is customizable for different choices of N. This proposed CROLoss formulation defines a more generalized loss function space, covering most of the conventional loss functions as special cases. Furthermore, we develop the Lambda method, a gradient-based method that invites more flexibility and can further boost the system performance. We evaluate the proposed CROLoss on two public benchmark datasets. The results show that CROLoss achieves SOTA results over conventional loss functions for both datasets with various choices of retrieval size N. CROLoss has been deployed onto our online E-commerce advertising platform, where a fourteen-day online A/B test demonstrated that CROLoss contributes to a significant business revenue growth of 4.75%.