research
∙
06/26/2023
Contrastive Multi-view Framework for Customer Lifetime Value Prediction
Accurate customer lifetime value (LTV) prediction can help service provi...
share
research
∙
03/15/2023
DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision
Algorithmic fairness has become an important machine learning problem, e...
research
∙
10/17/2022
Effective and Efficient Query-aware Snippet Extraction for Web Search
Query-aware webpage snippet extraction is widely used in search engines ...
research
∙
07/19/2022
FedX: Unsupervised Federated Learning with Cross Knowledge Distillation
This paper presents FedX, an unsupervised federated learning framework. ...
research
∙
06/07/2022
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning
Vertical federated learning (VFL) is a privacy-preserving machine learni...
research
∙
04/21/2022
FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation
Contrastive learning is widely used for recommendation model learning, w...
research
∙
04/18/2022
PrivateRec: Differentially Private Training and Serving for Federated News Recommendation
Privacy protection is an essential issue in personalized news recommenda...
research
∙
04/10/2022
FUM: Fine-grained and Fast User Modeling for News Recommendation
User modeling is important for news recommendation. Existing methods usu...
research
∙
04/10/2022
News Recommendation with Candidate-aware User Modeling
News recommendation aims to match news with personalized user interest. ...
research
∙
04/10/2022
ProFairRec: Provider Fairness-aware News Recommendation
News recommendation aims to help online news platform users find their p...
research
∙
04/01/2022
Unified and Effective Ensemble Knowledge Distillation
Ensemble knowledge distillation can extract knowledge from multiple teac...
research
∙
04/01/2022
FairRank: Fairness-aware Single-tower Ranking Framework for News Recommendation
Single-tower models are widely used in the ranking stage of news recomme...
research
∙
04/01/2022
End-to-end Learnable Diversity-aware News Recommendation
Diversity is an important factor in providing high-quality personalized ...
research
∙
04/01/2022
Semi-FairVAE: Semi-supervised Fair Representation Learning with Adversarial Variational Autoencoder
Adversarial learning is a widely used technique in fair representation l...
research
∙
02/28/2022
Are Big Recommendation Models Fair to Cold Users?
Big models are widely used by online recommender systems to boost recomm...
research
∙
02/28/2022
Quality-aware News Recommendation
News recommendation is a core technique used by many online news platfor...
research
∙
02/24/2022
NoisyTune: A Little Noise Can Help You Finetune Pretrained Language Models Better
Effectively finetuning pretrained language models (PLMs) is critical for...
research
∙
02/16/2022
No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices
Federated learning (FL) is an important paradigm for training global mod...
research
∙
02/10/2022
Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restriction
Vertical federated learning (VFL) aims to train models from cross-silo d...
research
∙
02/10/2022
FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling
Federated learning (FL) is a feasible technique to learn personalized re...
research
∙
12/02/2021
Tiny-NewsRec: Efficient and Effective PLM-based News Recommendation
Personalized news recommendation has been widely adopted to improve user...
research
∙
09/12/2021
Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation
News recommendation is critical for personalized news access. Most exist...
research
∙
09/11/2021
Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving
News recommendation is important for personalized online news services. ...
research
∙
09/03/2021
UserBERT: Contrastive User Model Pre-training
User modeling is critical for personalized web applications. Existing us...
research
∙
08/30/2021
FedKD: Communication Efficient Federated Learning via Knowledge Distillation
Federated learning is widely used to learn intelligent models from decen...
research
∙
08/20/2021
Smart Bird: Learnable Sparse Attention for Efficient and Effective Transformer
Transformer has achieved great success in NLP. However, the quadratic co...
research
∙
08/20/2021
Fastformer: Additive Attention Can Be All You Need
Transformer is a powerful model for text understanding. However, it is i...
research
∙
08/20/2021
Is News Recommendation a Sequential Recommendation Task?
News recommendation is often modeled as a sequential recommendation task...
research
∙
06/16/2021
Personalized News Recommendation: A Survey
Personalized news recommendation is an important technique to help users...
research
∙
06/11/2021
DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning
News recommendation is important for improving news reading experience o...
research
∙
06/08/2021
HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation
User interest modeling is critical for personalized news recommendation....
research
∙
06/02/2021
PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity
Personalized news recommendation methods are widely used in online news ...
research
∙
06/02/2021
Hi-Transformer: Hierarchical Interactive Transformer for Efficient and Effective Long Document Modeling
Transformer is important for text modeling. However, it has difficulty i...
research
∙
06/02/2021
One Teacher is Enough? Pre-trained Language Model Distillation from Multiple Teachers
Pre-trained language models (PLMs) achieve great success in NLP. However...
research
∙
05/27/2021
Rethinking InfoNCE: How Many Negative Samples Do You Need?
InfoNCE loss is a widely used loss function for contrastive model traini...
research
∙
04/20/2021
Personalized News Recommendation with Knowledge-aware Interactive Matching
The most important task in personalized news recommendation is accurate ...
research
∙
04/15/2021
Empowering News Recommendation with Pre-trained Language Models
Personalized news recommendation is an essential technique for online ne...
research
∙
04/15/2021
MM-Rec: Multimodal News Recommendation
Accurate news representation is critical for news recommendation. Most o...
research
∙
04/15/2021
Two Birds with One Stone: Unified Model Learning for Both Recall and Ranking in News Recommendation
Recall and ranking are two critical steps in personalized news recommend...
research
∙
04/15/2021
DebiasedRec: Bias-aware User Modeling and Click Prediction for Personalized News Recommendation
News recommendation is critical for personalized news access. Existing n...
research
∙
02/09/2021
FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation
Graph neural network (GNN) is widely used for recommendation to model hi...
research
∙
02/09/2021
FeedRec: News Feed Recommendation with Various User Feedbacks
Personalized news recommendation techniques are widely adopted by many o...
research
∙
02/09/2021
NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application
Pre-trained language models (PLMs) like BERT have made great progress in...
research
∙
01/12/2021
Neural News Recommendation with Negative Feedback
News recommendation is important for online news services. Precise user ...
research
∙
10/14/2020
DA-Transformer: Distance-aware Transformer
Transformer has achieved great success in the NLP field by composing var...
research
∙
10/08/2020
Improving Attention Mechanism with Query-Value Interaction
Attention mechanism has played critical roles in various state-of-the-ar...
research
∙
10/04/2020
PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision
User modeling is critical for many personalized web services. Many exist...
research
∙
07/23/2020
FedCTR: Federated Native Ad CTR Prediction with Multi-Platform User Behavior Data
Native ad is a popular type of online advertisement which has similar fo...
research
∙
06/30/2020
Fairness-aware News Recommendation with Decomposed Adversarial Learning
News recommendation is important for online news services. Most news rec...
research
∙
03/31/2020