13 papers
LACRM: LLM-Guided Mining of Directed Advertiser Competition Relationships in E-Commerce Advertising
Xiang Shi, Yitian Kou, Bing Li +6
In e-commerce advertising, ad platforms typically fail to model the externalities among advertisers when making optimization decisions: an advertiser's actual performance is largel…
LangBP: Language-Guided Reasoning and Acting for Joint Bidding and Pricing
Jiaqi Ding, Chuan Yang, Linghui Meng +5
Auto-bidding is a long-horizon sequential decision problem for maximizing conversion value under budget and key performance indicator (KPI) constraints. Recent work extends this ta…
JD-BP: A Joint-Decision Generative Framework for Auto-Bidding and Pricing
Linghui Meng, Chun Gan, Shengsheng Niu +8
Auto-bidding services optimize real-time bidding strategies for advertisers under key performance indicator (KPI) constraints such as target return on investment and budget. Howeve…
GCRank: A Generative Contextual Comprehension Paradigm for Takeout Ranking Model
Ziheng Ni, Congcong Liu, Cai Shang +10
The ranking stage serves as the central optimization and allocation hub in advertising systems, governing economic value distribution through eCPM and orchestrating the user-centri…
ADORE: Autonomous Domain-Oriented Relevance Engine for E-commerce
Zheng Fang, Donghao Xie, Ming Pang +5
Relevance modeling in e-commerce search remains challenged by semantic gaps in term-matching methods (e.g., BM25) and neural models' reliance on the scarcity of domain-specific har…
Uncertainty Modeling for Multi-Objective RTA Interception with Distillation Acceleration
Gaoxiang Zhao, Ruinan Qiu, Xiaoting Wang +4
Real-Time Auction (RTA) interception decides which incoming advertising requests reach downstream systems, and therefore controls the quality of the data those systems learn from.…
Auto-bidding under Return-on-Spend Constraints with Uncertainty Quantification
Jiale Han, Chun Gan, Chengcheng Zhang +4
Auto-bidding systems are widely used in advertising to automatically determine bid values under constraints such as total budget and Return-on-Spend (RoS) targets. Existing works o…
Generative Modeling with Multi-Instance Reward Learning for E-commerce Creative Optimization
Qiaolei Gu, Yu Li, DingYi Zeng +6
In e-commerce advertising, selecting the most compelling combination of creative elements -- such as titles, images, and highlights -- is critical for capturing user attention and…
Generative Click-through Rate Prediction with Applications to Search Advertising
Lingwei Kong, Lu Wang, Changping Peng +3
Click-Through Rate (CTR) prediction models are integral to a myriad of industrial settings, such as personalized search advertising. Current methods typically involve feature extra…
A Semi-supervised Scalable Unified Framework for E-commerce Query Classification
Chunyuan Yuan, Chong Zhang, Zheng Fang +5
Query classification, including multiple subtasks such as intent and category prediction, is vital to e-commerce applications. E-commerce queries are usually short and lack context…
Multi-objective Aligned Bidword Generation Model for E-commerce Search Advertising
Zhenhui Liu, Chunyuan Yuan, Ming Pang +7
Retrieval systems primarily address the challenge of matching user queries with the most relevant advertisements, playing a crucial role in e-commerce search advertising. The diver…
Generative Retrieval and Alignment Model: A New Paradigm for E-commerce Retrieval
Ming Pang, Chunyuan Yuan, Xiaoyu He +8
Traditional sparse and dense retrieval methods struggle to leverage general world knowledge and often fail to capture the nuanced features of queries and products. With the advent…
A Semi-supervised Multi-channel Graph Convolutional Network for Query Classification in E-commerce
Chunyuan Yuan, Ming Pang, Zheng Fang +3
Query intent classification is an essential module for customers to find desired products on the e-commerce application quickly. Most existing query intent classification methods r…