1 citations · 2 across the 9 of their papers we have counts for
9 papers
Generative Optimization for Incentivized Advertising with Global Level Constraints
Gege Chen, Ning Luo, Hao Jiang +7
Incentivized advertising allocates monetary or virtual rewards to drive user engagement, where a key challenge is optimizing continuous incentive magnitudes under strict global con…
HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising
Ji Wu, Yunshan Peng, Wentao Bai +5
Online advertising bidding systems typically deploy multiple offline-trained expert models (e.g., PID controllers, model predictive control, offline RL policies) but face two criti…
R&F-Inventory: A Large-Scale Dataset for Monotonic Inventory Estimation in Reach and Frequency Advertising
Yunshan Peng, Ji Wu, Wentao Bai +6
Reach and Frequency (R&F) contract advertising is an important form of widely used brand advertising. Unlike performance advertising, R&F contracts emphasize controllable delivery…
Expert-Guided Diffusion Planner for Auto-Bidding
Yunshan Peng, Wenzheng Shu, Jiahao Sun +6
Auto-bidding is widely used in advertising systems, serving a diverse range of advertisers. Generative bidding is increasingly gaining traction due to its strong planning capabilit…
VQL: An End-to-End Context-Aware Vector Quantization Attention for Ultra-Long User Behavior Modeling
Kaiyuan Li, Yongxiang Tang, Yanhua Cheng +5
In large-scale recommender systems, ultra-long user behavior sequences encode rich signals of evolving interests. Extending sequence length generally improves accuracy, but directl…
Aggregate and Broadcast: Scalable and Efficient Feature Interaction for Recommender Systems
Kaiyuan Li, Yongxiang Tang, Wenzheng Shu +5
Feature interaction is a core ingredient in ranking models for large-scale recommender systems, yet making it both expressive and efficiently scalable remains challenging. Exhausti…
Reward Balancing Revisited: Enhancing Offline Reinforcement Learning for Recommender Systems
Wenzheng Shu, Yanxiang Zeng, Yongxiang Tang +6
Offline reinforcement learning (RL) has emerged as a prevalent and effective methodology for real-world recommender systems, enabling learning policies from historical data and cap…
Optimal Return-to-Go Guided Decision Transformer for Auto-Bidding in Advertisement
Hao Jiang, Yongxiang Tang, Yanxiang Zeng +5
In the realm of online advertising, advertisers partake in ad auctions to obtain advertising slots, frequently taking advantage of auto-bidding tools provided by demand-side platfo…
Learning Monotonic Probabilities with a Generative Cost Model
Yongxiang Tang, Yanhua Cheng, Xiaocheng Liu +6
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 mono…