Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendation

Author:

Wu Xuansheng1ORCID,Zhou Huachi2ORCID,Shi Yucheng1ORCID,Yao Wenlin3ORCID,Huang Xiao2ORCID,Liu Ninghao1ORCID

Affiliation:

1. University of Georgia, Athens, Georgia, USA

2. The Hong Kong Polytechnic University, Hung Hom, Hong Kong

3. Tencent AI Lab, Seattle, Washington, USA

Publisher

ACM

Reference68 articles.

1. Leonard E Baum and Ted Petrie. 1966. Statistical inference for probabilistic functions of finite state Markov chains. The annals of mathematical statistics, Vol. 37, 6 (1966), 1554--1563.

2. Sid Black Leo Gao Phil Wang Connor Leahy and Stella Biderman. 2021. GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow. https://doi.org/10.5281/zenodo.5297715 If you use this software please cite it using these metadata. .

3. JesúS Bobadilla, Fernando Ortega, Antonio Hernando, and Jesús Bernal. 2012. A collaborative filtering approach to mitigate the new user cold start problem. Knowledge-based systems , Vol. 26 (2012), 225--238.

4. Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems Vol. 33 (2020) 1877--1901.

5. Zeyu Cui, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. 2022. M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems. arXiv preprint arXiv:2205.08084 (2022).

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1. Optimizing Novelty of Top-k Recommendations using Large Language Models and Reinforcement Learning;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

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