GRAM: Fast Fine-tuning of Pre-trained Language Models for Content-based Collaborative Filtering

Yoonseok Yang, Kyu Seok Kim, Minsam Kim, Juneyoung Park


Abstract
Content-based collaborative filtering (CCF) predicts user-item interactions based on both users’ interaction history and items’ content information. Recently, pre-trained language models (PLM) have been used to extract high-quality item encodings for CCF. However, it is resource-intensive to train a PLM-based CCF model in an end-to-end (E2E) manner, since optimization involves back-propagating through every content encoding within a given user interaction sequence. To tackle this issue, we propose GRAM (GRadient Accumulation for Multi-modality in CCF), which exploits the fact that a given item often appears multiple times within a batch of interaction histories. Specifically, Single-step GRAM aggregates each item encoding’s gradients for back-propagation, with theoretic equivalence to the standard E2E training. As an extension of Single-step GRAM, we propose Multi-step GRAM, which increases the gradient update latency, achieving a further speedup with drastically less GPU memory. GRAM significantly improves training efficiency (up to 146x) on five datasets from two task domains of Knowledge Tracing and News Recommendation. Our code is available at https://github.com/yoonseok312/GRAM.
Anthology ID:
2022.naacl-main.61
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
839–851
Language:
URL:
https://aclanthology.org/2022.naacl-main.61
DOI:
10.18653/v1/2022.naacl-main.61
Bibkey:
Cite (ACL):
Yoonseok Yang, Kyu Seok Kim, Minsam Kim, and Juneyoung Park. 2022. GRAM: Fast Fine-tuning of Pre-trained Language Models for Content-based Collaborative Filtering. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 839–851, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
GRAM: Fast Fine-tuning of Pre-trained Language Models for Content-based Collaborative Filtering (Yang et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.61.pdf
Code
 yoonseok312/gram