8 citations · 39 across the 52 of their papers we have counts for
17 papers · 1 filter
A Joint Auction Framework with Externalities and Adaptation
Chun Fang, Luowen Liu, Kun Huang +6
Recently, joint advertising has gained significant attention as an effective approach to enhancing the efficiency and revenue of advertising slot allocation. Unlike traditional adv…
Reassessing the Role of Supervised Fine-Tuning: An Empirical Study in VLM Reasoning
Yongcan Yu, Lingxiao He, Shuo Lu +10
Recent advances in vision-language models (VLMs) reasoning have been largely attributed to the rise of reinforcement Learning (RL), which has shifted the community's focus away fro…
RIA: A Ranking-Infused Approach for Optimized listwise CTR Prediction
Guoxiao Zhang, Tan Qu, Ao Li +3
Reranking improves recommendation quality by modeling item interactions. However, existing methods often decouple ranking and reranking, leading to weak listwise evaluation models…
FITRep: Attention-Guided Item Representation via MLLMs
Guoxiao Zhang, Ao Li, Tan Qu +2
Online platforms usually suffer from user experience degradation due to near-duplicate items with similar visuals and text. While Multimodal Large Language Models (MLLMs) enable mu…
AI-Salesman: Towards Reliable Large Language Model Driven Telemarketing
Qingyu Zhang, Chunlei Xin, Xuanang Chen +7
Goal-driven persuasive dialogue, exemplified by applications like telemarketing, requires sophisticated multi-turn planning and strict factual faithfulness, which remains a signifi…
HoMer: Addressing Heterogeneities by Modeling Sequential and Set-wise Contexts for CTR Prediction
Shuwei Chen, Jiajun Cui, Zhengqi Xu +4
Click-through rate (CTR) prediction, which models behavior sequence and non-sequential features (e.g., user/item profiles or cross features) to infer user interest, underpins indus…
Exploring Scaling Laws of CTR Model for Online Performance Improvement
Weijiang Lai, Beihong Jin, Jiongyan Zhang +5
CTR models play a vital role in improving user experience and boosting business revenue in many online personalized services. However, current CTR models generally encounter bottle…
Modeling Long-term User Behaviors with Diffusion-driven Multi-interest Network for CTR Prediction
Weijiang Lai, Beihong Jin, Yapeng Zhang +5
CTR (Click-Through Rate) prediction, crucial for recommender systems and online advertising, etc., has been confirmed to benefit from modeling long-term user behaviors. Nonetheless…
You Only Evaluate Once: A Tree-based Rerank Method at Meituan
Shuli Wang, Yinqiu Huang, Changhao Li +6
Reranking plays a crucial role in modern recommender systems by capturing the mutual influences within the list. Due to the inherent challenges of combinatorial search spaces, most…
On Negative-aware Preference Optimization for Recommendation
Chenlu Ding, Daoxuan Liu, Jiancan Wu +6
Recommendation systems leverage user interaction data to suggest relevant items while filtering out irrelevant (negative) ones. The rise of large language models (LLMs) has garnere…
Generative Bid Shading in Real-Time Bidding Advertising
Yinqiu Huang, Hao Ma, Wenshuai Chen +7
Bid shading plays a crucial role in Real-Time Bidding (RTB) by adaptively adjusting the bid to avoid advertisers overspending. Existing mainstream two-stage methods, which first mo…
Generative Large-Scale Pre-trained Models for Automated Ad Bidding Optimization
Yu Lei, Jiayang Zhao, Yilei Zhao +4
Modern auto-bidding systems are required to balance overall performance with diverse advertiser goals and real-world constraints, reflecting the dynamic and evolving needs of the i…
NGA: Non-autoregressive Generative Auction with Global Externalities for Advertising Systems
Zuowu Zheng, Ze Wang, Fan Yang +5
Online advertising auctions are fundamental to internet commerce, demanding solutions that not only maximize revenue but also ensure incentive compatibility, high-quality user expe…
EGA-V1: Unifying Online Advertising with End-to-End Learning
Junyan Qiu, Ze Wang, Fan Zhang +7
Modern industrial advertising systems commonly employ Multi-stage Cascading Architectures (MCA) to balance computational efficiency with ranking accuracy. However, this approach pr…
Deterministic-Allocation and Anonymous Joint Advertising in E-commerce Platforms
Zhen Zhang, Luowen Liu, Wanzhi Zhang +5
With the advancement of machine learning, an increasing number of studies are employing automated mechanism design (AMD) methods for optimal auction design. However, all previous A…
EGA-V2: An End-to-end Generative Framework for Industrial Advertising
Zuowu Zheng, Ze Wang, Fan Yang +4
Traditional online industrial advertising systems suffer from the limitations of multi-stage cascaded architectures, which often discard high-potential candidates prematurely and d…
NLGR: Utilizing Neighbor Lists for Generative Rerank in Personalized Recommendation Systems
Shuli Wang, Xue Wei, Senjie Kou +6
Reranking plays a crucial role in modern multi-stage recommender systems by rearranging the initial ranking list. Due to the inherent challenges of combinatorial search spaces, som…