most citedAlways Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction

1 citations · 1 across the 1 of their papers we have counts for

collaborators

5 papers

cs.LG2025

CTR-Driven Advertising Image Generation with Multimodal Large Language Models

Xingye Chen, Wei Feng, Zhenbang Du +16

In web data, advertising images are crucial for capturing user attention and improving advertising effectiveness. Most existing methods generate background for products primarily f…

cs.CV2024

Towards Reliable Advertising Image Generation Using Human Feedback

Zhenbang Du, Wei Feng, Haohan Wang +10

In the e-commerce realm, compelling advertising images are pivotal for attracting customer attention. While generative models automate image generation, they often produce substand…

cs.IR20233 cited

Rethinking Large-scale Pre-ranking System: Entire-chain Cross-domain Models

Jinbo Song, Ruoran Huang, Xinyang Wang +9

Industrial systems such as recommender systems and online advertising, have been widely equipped with multi-stage architectures, which are divided into several cascaded modules, in…

cs.IR2023

Towards Better Query Classification with Multi-Expert Knowledge Condensation in JD Ads Search

Kun-Peng Ning, Ming Pang, Zheng Fang +6

Search query classification, as an effective way to understand user intents, is of great importance in real-world online ads systems. To ensure a lower latency, a shallow model (e.…

cs.IR20231 cited

Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction

Congcong Liu, Fei Teng, Xiwei Zhao +3

Click-through rate (CTR) prediction is of great importance in recommendation systems and online advertising platforms. When served in industrial scenarios, the user-generated data…