4 citations · 4 across the 2 of their papers we have counts for
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cs.IR2024
TokenRec: Learning to Tokenize ID for LLM-based Generative Recommendation
Haohao Qu, Wenqi Fan, Zihuai Zhao +1
There is a growing interest in utilizing large-scale language models (LLMs) to advance next-generation Recommender Systems (RecSys), driven by their outstanding language understand…
cs.IR2023
TF-DCon: Leveraging Large Language Models (LLMs) to Empower Training-Free Dataset Condensation for Content-Based Recommendation
Jiahao Wu, Qijiong Liu, Hengchang Hu +5
Modern techniques in Content-based Recommendation (CBR) leverage item content information to provide personalized services to users, but suffer from resource-intensive training on…
cs.IR2023
Dataset Condensation for Recommendation
Jiahao Wu, Wenqi Fan, Jingfan Chen +5
Training recommendation models on large datasets requires significant time and resources. It is desired to construct concise yet informative datasets for efficient training. Recent…