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Yuchao Ma

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5 papers

1 author row

AAAI Conference 2026 Conference Paper

SACO: Sequence-Aware Constrained Optimization Framework for Coupon Distribution in E-commerce

Li Kong
Bingzhe Wang
Zhou Chen
Suhan Hu
Yuchao Ma
Qi Qi
Suoyuan Song
Bicheng Jin

Coupon distribution is a critical marketing strategy used by online platforms to boost revenue and enhance user engagement. Regrettably, existing coupon distribution strategies fall far short of effectively leveraging the complex sequential interactions between platforms and users. This critical oversight, despite the abundance of e-commerce log data, has precipitated a performance plateau. In this paper, we focus on the scene that the platforms make sequential coupon distribution decision multiple times for various users, with each user interacting with the platform repeatedly. Based on this marketing scenario, we propose a novel marketing framework, named Sequence-Aware Constrained Optimization (SACO) framework, to directly devise coupon distribution policy for long-term revenue boosting. SACO framework enables optimized online decision-making in a variety of real-world marketing scenarios. It achieves this by seamlessly integrating three key characteristics, general scenarios, sequential modeling with more comprehensive historical data, and efficient iterative updates within a unified framework. Furthermore, empirical results on real-world industrial dataset, alongside public and synthetic datasets demonstrate the superiority of our framework.

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AAAI Conference 2025 Conference Paper

GenAuction: A Generative Auction for Online Advertising

Yuchao Ma
Ruohan Qian
Bingzhe Wang
Qi Qi
Wenqiang Liu
Qian Tang
Zhao Shen
Wei Zhong

Previous ad auctions predominantly relied on rule-based mechanisms, which selected winning advertisements (ads) at the ad-level and subsequently combined them into page views (PVs), leading to suboptimal allocations in multi-round auctions. This limitation stems from the significant computational burden required to design ranking score rules and select winning ad sets, as well as the inability to fully capture contextual information within PVs during ad-level selection. In this paper, we propose a key-performance-indicator (KPI) based auction mechanism that selects winning PVs at the PV-level, modeling the ad allocation as a constrained optimization problem. This approach enables us to address both short-term and long-term KPIs while leveraging the comprehensive contextual information available within PVs. Based on this framework, we design GenAuction, a generative auction mechanism utilizing a Generator-Evaluator architecture powered by Transformer algorithms. The Generator swiftly generates multiple candidate PVs, while the Evaluator selects the optimal PVs based on contextual information, adhering to the objectives and KPIs of multi-round auctions. We conduct extensive experiments using real-world data and online A/B tests to validate that GenAuction efficiently handles multi-objective allocation tasks, demonstrating its efficacy and potential for real-world application.

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TCS Journal 2025 Journal Article

Joint bidding in ad auctions

Yuchao Ma
Weian Li
Wanzhi Zhang
Yahui Lei
Zhicheng Zhang
Qi Qi
Qiang Liu
Xingxing Wang

In traditional advertising auctions, commodity suppliers as advertisers compete for adverting positions to display commodities. As e-commerce platforms become more prevalent, offline retailers are also opening online virtual shops, and retailers are starting to pay a fee for extra exposure of their shops. This has led to situations where a single commodity may be sponsored by both the retailer and the supplier, offering opportunities for more profit. In order to explore this novel advertising pattern, we propose a new model called the joint advertising system (JAS), where retailers and suppliers jointly bid for advertising positions. In the context of this realistic scenario, conventional mechanisms such as GFP, GSP and Myerson auction cannot be applied directly. Besides, the VCG mechanism results in negative revenue in JAS. To solve this issue, we modify the payment rule of VCG to create a revised VCG mechanism that guarantees incentive compatible, individually rational and weakly budget-balanced. Additionally, we leverage the structure of the affine maximizer auction (AMA) and the technique of automated mechanism design to train joint AMA. Finally, we conduct several experiments to demonstrate the performance of the joint AMA. It turns out that our mechanism maintains good economic properties and outperforms other mechanisms in various settings.

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AAAI Conference 2025 Conference Paper

On Designing the Optimal Integrated Ad Auction in E-commerce Platforms

Yuchao Ma
Weian Li
Yuhan Wang
Zitian Guo
Yuejia Dou
Qi Qi
Changyuan Yu

Currently, e-commerce platforms integrate ads and organic content into a mixed list for users. While platforms seek to maximize profit from advertisers, organic items enhance user experience. To ensure long-term development, platforms aim to design mechanisms that optimize both revenue and user satisfaction. Current methods rank ads and organic items separately before integrating them. Even if each part is locally optimal, the combined result may not be globally optimal. In this paper, we come up with the Joint Integrated Regret Network (JINTER Net). Unlike traditional methods, which pre-order ads and organic items separately, JINTER Net directly selects from the combined set of candidate ads and organic items to generate an optimal list. This approach aims to optimally balance platform revenue and user experience while satisfying approximate dominant strategy incentive compatibility and individual rationality. We validate the effectiveness of JINTER Net using both synthetic data and real dataset, and our experimental results show that it significantly outperforms baseline models across multiple metrics.

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AAAI Conference 2019 Conference Paper

LabelForest: Non-Parametric Semi-Supervised Learning for Activity Recognition

Yuchao Ma
Hassan Ghasemzadeh

Activity recognition is central to many motion analysis applications ranging from health assessment to gaming. However, the need for obtaining sufficiently large amounts of labeled data has limited the development of personalized activity recognition models. Semi-supervised learning has traditionally been a promising approach in many application domains to alleviate reliance on large amounts of labeled data by learning the label information from a small set of seed labels. Nonetheless, existing approaches perform poorly in highly dynamic settings, such as wearable systems, because some algorithms rely on predefined hyper-parameters or distribution models that needs to be tuned for each user or context. To address these challenges, we introduce LabelForest 1, a novel non-parametric semi-supervised learning framework for activity recognition. LabelForest has two algorithms at its core: (1) a spanning forest algorithm for sample selection and label inference; and (2) a silhouette-based filtering method to finalize label augmentation for machine learning model training. Our thorough analysis on three human activity datasets demonstrate that LabelForest achieves a labeling accuracy of 90. 1% in presence of a skewed label distribution in the seed data. Compared to self-training and other sequential learning algorithms, LabelForest achieves up to 56. 9% and 175. 3% improvement in the accuracy on balanced and unbalanced seed data, respectively.

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