Bridging Items and Language: A Transition Paradigm for Large Language Model-Based Recommendation

Author:

Lin Xinyu1ORCID,Wang Wenjie1ORCID,Li Yongqi2ORCID,Feng Fuli3ORCID,Ng See-Kiong1ORCID,Chua Tat-Seng1ORCID

Affiliation:

1. National University of Singapore, Singapore, Singapore

2. The Hong Kong Polytechnic University, Hong Kong SAR, China

3. University of Science and Technology of China, Hefei, China

Funder

National Natural Science Foundation of China

Publisher

ACM

Reference67 articles.

1. Keqin Bao Jizhi Zhang Wenjie Wang Yang Zhang Zhengyi Yang Yancheng Luo Fuli Feng Xiangnaan He and Qi Tian. 2023. A bi-step grounding paradigm for large language models in recommendation systems. arXiv:2308.08434.

2. Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023. Tallrec: An effective and efficient tuning framework to align large language model with recommendation. In RecSys. ACM.

3. William Berrios Gautam Mittal Tristan Thrush Douwe Kiela and Amanpreet Singh. 2023. Towards language models that can see: Computer vision through the lens of natural language. arXiv:2306.16410.

4. Autoregressive search engines: Generating substrings as document identifiers;Bevilacqua Michele;NeurIPS,2022

5. Vadim Borisov Kathrin Seßler Tobias Leemann Martin Pawelczyk and Gjergji Kasneci. 2022. Language models are realistic tabular data generators. arXiv:2210.06280.

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