Learnable Item Tokenization for Generative Recommendation

Utilizing powerful Large Language Models (LLMs) for generative recommendation has attracted much attention. Nevertheless, a crucial challenge is transforming recommendation data into the language space of LLMs through effective item tokenization. Current approaches, such as ID, textual, and codebook...

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Veröffentlicht in:	arXiv.org 2024-08
Hauptverfasser:	Wang, Wenjie, Bao, Honghui, Lin, Xinyu, Zhang, Jizhi, Li, Yongqi, Feng, Fuli, See-Kiong Ng, Tat-Seng Chua
Format:	Artikel
Sprache:	eng
Schlagworte:	Bias Collaboration Large language models Ranking Regularization Semantics
Online-Zugang:	Volltext
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