623 citations · 786 across the 21 of their papers we have counts for
21 papers
GLoCIM: Global-view Long Chain Interest Modeling for news recommendation
Zhen Yang, Wenhui Wang, Tao Qi +5
Accurately recommending candidate news articles to users has always been the core challenge of news recommendation system. News recommendations often require modeling of user inter…
Specializing Small Language Models towards Complex Style Transfer via Latent Attribute Pre-Training
Ruiqi Xu, Yongfeng Huang, Xin Chen +1
In this work, we introduce the concept of complex text style transfer tasks, and constructed complex text datasets based on two widely applicable scenarios. Our dataset is the firs…
FoodGPT: A Large Language Model in Food Testing Domain with Incremental Pre-training and Knowledge Graph Prompt
Zhixiao Qi, Yijiong Yu, Meiqi Tu +2
Currently, the construction of large language models in specific domains is done by fine-tuning on a base model. Some models also incorporate knowledge bases without the need for p…
CLEVA: Chinese Language Models EVAluation Platform
Yanyang Li, Jianqiao Zhao, Duo Zheng +8
With the continuous emergence of Chinese Large Language Models (LLMs), how to evaluate a model's capabilities has become an increasingly significant issue. The absence of a compreh…
FedSampling: A Better Sampling Strategy for Federated Learning
Tao Qi, Fangzhao Wu, Lingjuan Lyu +2
Federated learning (FL) is an important technique for learning models from decentralized data in a privacy-preserving way. Existing FL methods usually uniformly sample clients for…
Solving Math Word Problems via Cooperative Reasoning induced Language Models
Xinyu Zhu, Junjie Wang, Lin Zhang +4
Large-scale pre-trained language models (PLMs) bring new opportunities to challenging problems, especially those that need high-level intelligence, such as the math word problem (M…
Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence
Jiaxing Zhang, Ruyi Gan, Junjie Wang +22
Nowadays, foundation models become one of fundamental infrastructures in artificial intelligence, paving ways to the general intelligence. However, the reality presents two urgent…
Semi-Supervised Hierarchical Graph Classification
Jia Li, Yongfeng Huang, Heng Chang +1
Node classification and graph classification are two graph learning problems that predict the class label of a node and the class label of a graph respectively. A node of a graph u…
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning
Tao Qi, Fangzhao Wu, Chuhan Wu +5
Vertical federated learning (VFL) is a privacy-preserving machine learning paradigm that can learn models from features distributed on different platforms in a privacy-preserving w…
FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation
Chuhan Wu, Fangzhao Wu, Tao Qi +2
Contrastive learning is widely used for recommendation model learning, where selecting representative and informative negative samples is critical. Existing methods usually focus o…
FUM: Fine-grained and Fast User Modeling for News Recommendation
Tao Qi, Fangzhao Wu, Chuhan Wu +1
User modeling is important for news recommendation. Existing methods usually first encode user's clicked news into news embeddings independently and then aggregate them into user e…
News Recommendation with Candidate-aware User Modeling
Tao Qi, Fangzhao Wu, Chuhan Wu +1
News recommendation aims to match news with personalized user interest. Existing methods for news recommendation usually model user interest from historical clicked news without th…
ProFairRec: Provider Fairness-aware News Recommendation
Tao Qi, Fangzhao Wu, Chuhan Wu +5
News recommendation aims to help online news platform users find their preferred news articles. Existing news recommendation methods usually learn models from historical user behav…
Unified and Effective Ensemble Knowledge Distillation
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Ensemble knowledge distillation can extract knowledge from multiple teacher models and encode it into a single student model. Many existing methods learn and distill the student mo…
FairRank: Fairness-aware Single-tower Ranking Framework for News Recommendation
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Single-tower models are widely used in the ranking stage of news recommendation to accurately rank candidate news according to their fine-grained relatedness with user interest ind…
End-to-end Learnable Diversity-aware News Recommendation
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Diversity is an important factor in providing high-quality personalized news recommendations. However, most existing news recommendation methods only aim to optimize recommendation…
Semi-FairVAE: Semi-supervised Fair Representation Learning with Adversarial Variational Autoencoder
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Adversarial learning is a widely used technique in fair representation learning to remove the biases on sensitive attributes from data representations. It usually requires to incor…
Are Big Recommendation Models Fair to Cold Users?
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Big models are widely used by online recommender systems to boost recommendation performance. They are usually learned on historical user behavior data to infer user interest and p…
Quality-aware News Recommendation
Chuhan Wu, Fangzhao Wu, Tao Qi +1
News recommendation is a core technique used by many online news platforms. Recommending high-quality news to users is important for keeping good user experiences and news platform…
FedKD: Communication Efficient Federated Learning via Knowledge Distillation
Chuhan Wu, Fangzhao Wu, Lingjuan Lyu +2
Federated learning is widely used to learn intelligent models from decentralized data. In federated learning, clients need to communicate their local model updates in each iteratio…
Personalized News Recommendation: Methods and Challenges
Chuhan Wu, Fangzhao Wu, Yongfeng Huang +1
Personalized news recommendation is an important technique to help users find their interested news information and alleviate their information overload. It has been extensively st…