9 citations · 33 across the 21 of their papers we have counts for
6 papers · 1 filter
Retrieval Augmented Conversational Recommendation with Reinforcement Learning
Zhenrui Yue, Honglei Zhuang, Zhen Qin +4
Large language models (LLMs) exhibit enhanced capabilities in language understanding and generation. By utilizing their embedded knowledge, LLMs are increasingly used as conversati…
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…
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…
Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders
Zhenrui Yue, Huimin Zeng, Ziyi Kou +2
While sequential recommender systems achieve significant improvements on capturing user dynamics, we argue that sequential recommenders are vulnerable against substitution-based pr…