6 citations · 17 across the 6 of their papers we have counts for
6 papers
SIGMA: A Semantic-Grounded Instruction-Driven Generative Multi-Task Recommender at AliExpress
Yang Yu, Lei Kou, Huaikuan Yi +6
With the rapid evolution of Large Language Models (LLMs), generative recommendation is gradually reshaping the paradigm of recommender systems. However, most existing methods remai…
Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense
Yang Yu, Qi Liu, Likang Wu +3
Federated recommendation (FedRec) can train personalized recommenders without collecting user data, but the decentralized nature makes it susceptible to poisoning attacks. Most pre…
Tiny-NewsRec: Effective and Efficient PLM-based News Recommendation
Yang Yu, Fangzhao Wu, Chuhan Wu +2
News recommendation is a widely adopted technique to provide personalized news feeds for the user. Recently, pre-trained language models (PLMs) have demonstrated the great capabili…
UserBERT: Contrastive User Model Pre-training
Chuhan Wu, Fangzhao Wu, Yang Yu +3
User modeling is critical for personalized web applications. Existing user modeling methods usually train user models from user behaviors with task-specific labeled data. However,…
HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation
Tao Qi, Fangzhao Wu, Chuhan Wu +4
User interest modeling is critical for personalized news recommendation. Existing news recommendation methods usually learn a single user embedding for each user from their previou…
NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application
Chuhan Wu, Fangzhao Wu, Yang Yu +3
Pre-trained language models (PLMs) like BERT have made great progress in NLP. News articles usually contain rich textual information, and PLMs have the potentials to enhance news t…