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8 papers · 1 filter

cs.CL2025

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…

cs.LG2025

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.…

cs.LG2025

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…

cs.LG2025

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…

cs.LG2025

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…

cs.CL2025

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…

cs.CL2025

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…

cs.IR2025

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…