271 citations · 574 across the 44 of their papers we have counts for
10 papers · 1 filter
One-Time Model Adaptation to Heterogeneous Clients: An Intra-Client and Inter-Image Attention Design
Yikai Yan, Chaoyue Niu, Fan Wu +4
The mainstream workflow of image recognition applications is first training one global model on the cloud for a wide range of classes and then serving numerous clients, each with h…
AutoAttention: Automatic Field Pair Selection for Attention in User Behavior Modeling
Zuowu Zheng, Xiaofeng Gao, Junwei Pan +4
In Click-through rate (CTR) prediction models, a user's interest is usually represented as a fixed-length vector based on her history behaviors. Recently, several methods are propo…
On-Device Model Fine-Tuning with Label Correction in Recommender Systems
Yucheng Ding, Chaoyue Niu, Fan Wu +3
To meet the practical requirements of low latency, low cost, and good privacy in online intelligent services, more and more deep learning models are offloaded from the cloud to mob…
To Store or Not? Online Data Selection for Federated Learning with Limited Storage
Chen Gong, Zhenzhe Zheng, Yunfeng Shao +3
Machine learning models have been deployed in mobile networks to deal with massive data from different layers to enable automated network management and intelligence on devices. To…
HIEN: Hierarchical Intention Embedding Network for Click-Through Rate Prediction
Zuowu Zheng, Changwang Zhang, Xiaofeng Gao +1
Click-through rate (CTR) prediction plays an important role in online advertising and recommendation systems, which aims at estimating the probability of a user clicking on a speci…
Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning
Chengfei Lv, Chaoyue Niu, Renjie Gu +17
To break the bottlenecks of mainstream cloud-based machine learning (ML) paradigm, we adopt device-cloud collaborative ML and build the first end-to-end and general-purpose system,…
Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach
Chenxiao Yang, Qitian Wu, Jipeng Jin +3
Collaborative filtering (CF), as a standard method for recommendation with implicit feedback, tackles a semi-supervised learning problem where most interaction data are unobserved.…
Cross-Task Knowledge Distillation in Multi-Task Recommendation
Chenxiao Yang, Junwei Pan, Xiaofeng Gao +3
Multi-task learning (MTL) has been widely used in recommender systems, wherein predicting each type of user feedback on items (e.g, click, purchase) are treated as individual tasks…
Vertical Federated Learning: Challenges, Methodologies and Experiments
Kang Wei, Jun Li, Chuan Ma +5
Recently, federated learning (FL) has emerged as a promising distributed machine learning (ML) technology, owing to the advancing computational and sensing capacities of end-user d…
On-Device Learning with Cloud-Coordinated Data Augmentation for Extreme Model Personalization in Recommender Systems
Renjie Gu, Chaoyue Niu, Yikai Yan +5
Data heterogeneity is an intrinsic property of recommender systems, making models trained over the global data on the cloud, which is the mainstream in industry, non-optimal to eac…