271 citations · 574 across the 44 of their papers we have counts for
44 papers
Temporal Interest Network for User Response Prediction
Haolin Zhou, Junwei Pan, Xinyi Zhou +4
User response prediction is essential in industrial recommendation systems, such as online display advertising. Among all the features in recommendation models, user behaviors are…
DC-CCL: Device-Cloud Collaborative Controlled Learning for Large Vision Models
Yucheng Ding, Chaoyue Niu, Fan Wu +3
Many large vision models have been deployed on the cloud for real-time services. Meanwhile, fresh samples are continuously generated on the served mobile device. How to leverage th…
Truthful Auctions for Automated Bidding in Online Advertising
Yidan Xing, Zhilin Zhang, Zhenzhe Zheng +4
Automated bidding, an emerging intelligent decision making paradigm powered by machine learning, has become popular in online advertising. Advertisers in automated bidding evaluate…
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…
FastCover: An Unsupervised Learning Framework for Multi-Hop Influence Maximization in Social Networks
Runbo Ni, Xueyan Li, Fangqi Li +2
Finding influential users in social networks is a fundamental problem with many possible useful applications. Viewing the social network as a graph, the influence of a set of users…
Federated Submodel Optimization for Hot and Cold Data Features
Yucheng Ding, Chaoyue Niu, Fan Wu +4
We study practical data characteristics underlying federated learning, where non-i.i.d. data from clients have sparse features, and a certain client's local data normally involves…
Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising
Xiangyu Liu, Chuan Yu, Zhilin Zhang +10
In e-commerce advertising, it is crucial to jointly consider various performance metrics, e.g., user experience, advertiser utility, and platform revenue. Traditional auction mecha…
Hash Adaptive Bloom Filter
Rongbiao Xie, Meng Li, Zheyu Miao +4
Bloom filter is a compact memory-efficient probabilistic data structure supporting membership testing, i.e., to check whether an element is in a given set. However, as Bloom filter…
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising
Chao Wen, Miao Xu, Zhilin Zhang +12
In online advertising, auto-bidding has become an essential tool for advertisers to optimize their preferred ad performance metrics by simply expressing high-level campaign objecti…
Toward Understanding the Influence of Individual Clients in Federated Learning
Yihao Xue, Chaoyue Niu, Zhenzhe Zheng +4
Federated learning allows mobile clients to jointly train a global model without sending their private data to a central server. Extensive works have studied the performance guaran…
Neighborhood Matters: Influence Maximization in Social Networks with Limited Access
Chen Feng, Luoyi Fu, Bo Jiang +4
Influence maximization (IM) aims at maximizing the spread of influence by offering discounts to influential users (called seeding). In many applications, due to user's privacy conc…
Distributed Optimization over Block-Cyclic Data
Yucheng Ding, Chaoyue Niu, Yikai Yan +5
We consider practical data characteristics underlying federated learning, where unbalanced and non-i.i.d. data from clients have a block-cyclic structure: each cycle contains sever…
MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding
Haolin Zhou, Chaoqi Yang, Xiaofeng Gao +3
Online Real-Time Bidding (RTB) is a complex auction game among which advertisers struggle to bid for ad impressions when a user request occurs. Considering display cost, Return on…
Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability
Yikai Yan, Chaoyue Niu, Yucheng Ding +5
Federated learning is a new distributed machine learning framework, where a bunch of heterogeneous clients collaboratively train a model without sharing training data. In this work…
Online Pricing with Reserve Price Constraint for Personal Data Markets
Chaoyue Niu, Zhenzhe Zheng, Fan Wu +2
The society's insatiable appetites for personal data are driving the emergency of data markets, allowing data consumers to launch customized queries over the datasets collected by…