14 citations · 18 across the 9 of their papers we have counts for
9 papers
Transferable Sequential Recommendation via Vector Quantized Meta Learning
Zhenrui Yue, Huimin Zeng, Yang Zhang +2
While sequential recommendation achieves significant progress on capturing user-item transition patterns, transferring such large-scale recommender systems remains challenging due…
Train Once, Deploy Anywhere: Matryoshka Representation Learning for Multimodal Recommendation
Yueqi Wang, Zhenrui Yue, Huimin Zeng +2
Despite recent advancements in language and vision modeling, integrating rich multimodal knowledge into recommender systems continues to pose significant challenges. This is primar…
Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments
Zhenrui Yue, Huimin Zeng, Lanyu Shang +3
The rapid propagation of misinformation poses substantial risks to public interest. To combat misinformation, large language models (LLMs) are adapted to automatically verify claim…
Your Causal Self-Attentive Recommender Hosts a Lonely Neighborhood
Yueqi Wang, Zhankui He, Zhenrui Yue +2
In the context of sequential recommendation, a pivotal issue pertains to the comparative analysis between bi-directional/auto-encoding (AE) and uni-directional/auto-regressive (AR)…
Open-Vocabulary Federated Learning with Multimodal Prototyping
Huimin Zeng, Zhenrui Yue, Dong Wang
Existing federated learning (FL) studies usually assume the training label space and test label space are identical. However, in real-world applications, this assumption is too ide…
Evidence-Driven Retrieval Augmented Response Generation for Online Misinformation
Zhenrui Yue, Huimin Zeng, Yimeng Lu +3
The proliferation of online misinformation has posed significant threats to public interest. While numerous online users actively participate in the combat against misinformation,…
Federated Recommendation via Hybrid Retrieval Augmented Generation
Huimin Zeng, Zhenrui Yue, Qian Jiang +1
Federated Recommendation (FR) emerges as a novel paradigm that enables privacy-preserving recommendations. However, traditional FR systems usually represent users/items with discre…
Linear Recurrent Units for Sequential Recommendation
Zhenrui Yue, Yueqi Wang, Zhankui He +3
State-of-the-art sequential recommendation relies heavily on self-attention-based recommender models. Yet such models are computationally expensive and often too slow for real-time…
LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking
Zhenrui Yue, Sara Rabhi, Gabriel de Souza Pereira Moreira +2
Recently, large language models (LLMs) have exhibited significant progress in language understanding and generation. By leveraging textual features, customized LLMs are also applie…