most citedLlamaRec: Two-Stage Recommendation using Large Language Models for Ranking

14 citations · 18 across the 8 of their papers we have counts for

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cs.IR2024

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…

cs.IR2024

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…

cs.IR2024

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)…

cs.IR2024

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…

cs.IR20231 cited

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…

cs.IR202314 cited

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…