Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News Recommendation

Qijiong Liu, Jieming Zhu, Quanyu Dai, Xiaoming Wu


Abstract
Understanding news content is critical to improving the quality of news recommendation. To achieve this goal, recent studies have attempted to apply pre-trained language models (PLMs) such as BERT for semantic-enhanced news recommendation. Despite their great success in offline evaluation, it is still a challenge to apply such large PLMs in real-time ranking model due to the stringent requirement in inference and updating time. To bridge this gap, we propose a plug-and-play pre-trainer, namely PREC, to learn both user and news encoders through multi-task pre-training. Instead of directly leveraging sophisticated PLMs for end-to-end inference, we focus on how to use the derived user and item representations to boost the performance of conventional lightweight models for click-through-rate prediction. This enables efficient online inference as well as compatibility to conventional models, which would significantly ease the practical deployment. We validate the effectiveness of PREC through both offline evaluation on public datasets and online A/B testing in an industrial application.
Anthology ID:
2022.coling-1.249
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2823–2833
Language:
URL:
https://aclanthology.org/2022.coling-1.249
DOI:
Bibkey:
Cite (ACL):
Qijiong Liu, Jieming Zhu, Quanyu Dai, and Xiaoming Wu. 2022. Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News Recommendation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2823–2833, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News Recommendation (Liu et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.249.pdf
Code
 jyonn/prec