Harnessing Large Language Models for Text-Rich Sequential Recommendation-Reference-Cited by-同舟云学术

Harnessing Large Language Models for Text-Rich Sequential Recommendation

Published:2024-05-13 Issue: Volume:1 Page:3207-3216
ISSN:
Container-title:Proceedings of the ACM Web Conference 2024
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Short-container-title:

Author:

Zheng Zhi¹^ORCID,Chao WenShuo²^ORCID,Qiu Zhaopeng³^ORCID,Zhu Hengshu⁴^ORCID,Xiong Hui⁵^ORCID

Affiliation:

1. School of Data Science, University of Science and Technology of China & Career Science Lab, BOSS Zhipin, Hefei, China

2. Hong Kong University of Science and Technology (Guangzhou) & Career Science Lab, BOSS Zhipin, Guangzhou, China

3. Career Science Lab, BOSS Zhipin, Beijing, China

4. Career Science Lab, BOSS Zhipin & Thrust of Artificial Intelligence, Hong Kong University of Science and Technology (Guangzhou), Beijing, China

5. Thrust of Artificial Intelligence, Hong Kong University of Science and Technology (Guangzhou) & Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Guangzhou, China

Funder

Guangzhou-HKUST(GZ) Joint Funding Program

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589334.3645358

Reference49 articles.

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3. Stella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley, Kyle O'Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, et al. 2023. Pythia: A suite for analyzing large language models across training and scaling. In International Conference on Machine Learning. PMLR, 2397--2430.

4. Predicting the Popularity of Online Serials with Autoregressive Models

5. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).

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