2 citations · 3 across the 12 of their papers we have counts for
13 papers
Auto-Bidding with Disentangled Advertiser Profiles and Train-Free Adaptation
Songyue Cai, Shan Gu, Wei Chen +4
Auto-bidding is a key component of modern advertising systems that provides a personalized bidding strategy for each advertiser. By characterizing each individual, profile-based me…
Strategy-Aware Parameter-Efficient Adaptation for LLM-based Auto-Bidding
Songyue Cai, Lianyu Wang, Shan Gu +4
Advertising bidding has evolved from manual strategies to auto-bidding systems better adapted for large-scale, dynamic auction environments. While recent advances in Large Language…
TransRetrieval: Scaling Up Transformer-Based Retrieval for Industrial Recommendation
Zhifei Zheng, Yunfei Liu, Bin Liu +7
Applying scaling laws to recommendation retrieval is hindered by feature heterogeneity: naively stacking Transformer layers yields diminishing returns because heterogeneous fields…
MUSE: A Simple Yet Effective Multimodal Search-Based Framework for Lifelong User Interest Modeling
Bin Wu, Feifan Yang, Zhangming Chan +8
Lifelong user interest modeling is crucial for industrial recommender systems, yet existing approaches rely predominantly on ID-based features, suffering from poor generalization o…
AIF: Asynchronous Inference Framework for Cost-Effective Pre-Ranking
Zhi Kou, Xiang-Rong Sheng, Shuguang Han +5
In industrial recommendation systems, pre-ranking models based on deep neural networks (DNNs) commonly adopt a sequential execution framework: feature fetching and model forward co…
Think before Recommendation: Autonomous Reasoning-enhanced Recommender
Xiaoyu Kong, Junguang Jiang, Bin Liu +6
The core task of recommender systems is to learn user preferences from historical user-item interactions. With the rapid development of large language models (LLMs), recent researc…
Reinforced Preference Optimization for Recommendation
Junfei Tan, Yuxin Chen, An Zhang +7
Recent breakthroughs in large language models (LLMs) have fundamentally shifted recommender systems from discriminative to generative paradigms, where user behavior modeling is ach…
See Beyond a Single View: Multi-Attribution Learning Leads to Better Conversion Rate Prediction
Sishuo Chen, Zhangming Chan, Xiang-Rong Sheng +6
Conversion rate (CVR) prediction is a core component of online advertising systems, where the attribution mechanisms-rules for allocating conversion credit across user touchpoints-…
Bidding-Aware Retrieval for Multi-Stage Consistency in Online Advertising
Bin Liu, Yunfei Liu, Ziru Xu +4
Online advertising systems typically use a cascaded architecture to manage massive requests and candidate volumes, where the ranking stages allocate traffic based on eCPM (predicte…
Large Language Model as Universal Retriever in Industrial-Scale Recommender System
Junguang Jiang, Yanwen Huang, Bin Liu +6
In real-world recommender systems, different retrieval objectives are typically addressed using task-specific datasets with carefully designed model architectures. We demonstrate t…
Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches and Insights
Xiang-Rong Sheng, Feifan Yang, Litong Gong +10
Despite the recognized potential of multimodal data to improve model accuracy, many large-scale industrial recommendation systems, including Taobao display advertising system, pred…
Calibration-compatible Listwise Distillation of Privileged Features for CTR Prediction
Xiaoqiang Gui, Yueyao Cheng, Xiang-Rong Sheng +6
In machine learning systems, privileged features refer to the features that are available during offline training but inaccessible for online serving. Previous studies have recogni…
Robust Representation Learning for Unified Online Top-K Recommendation
Minfang Lu, Yuchen Jiang, Huihui Dong +9
In large-scale industrial e-commerce, the efficiency of an online recommendation system is crucial in delivering highly relevant item/content advertising that caters to diverse bus…