Breaking the Length Barrier: LLM-Enhanced CTR Prediction in Long Textual User Behaviors

Author:

Geng Binzong1ORCID,Huan Zhaoxin1ORCID,Zhang Xiaolu1ORCID,He Yong1ORCID,Zhang Liang1ORCID,Yuan Fajie2ORCID,Zhou Jun1ORCID,Mo Linjian1ORCID

Affiliation:

1. Ant Group, Hangzhou, China

2. Westlake University, Hangzhou, China

Publisher

ACM

Reference26 articles.

1. Andrew P Bradley. 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern recognition, Vol. 30, 7 (1997), 1145--1159.

2. 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.

3. Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction

4. 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).

5. Junchen Fu, Fajie Yuan, Yu Song, Zheng Yuan, Mingyue Cheng, Shenghui Cheng, Jiaqi Zhang, Jie Wang, and Yunzhu Pan. 2023 b. Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights. arXiv preprint arXiv:2305.15036 (2023).

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