GRAM: Fast Fine-tuning of Pre-trained Language Models for Content-based Collaborative Filtering. (arXiv:2204.04179v1 [cs.CL])

Content-based collaborative filtering (CCF) provides personalized item
recommendations 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 finetune PLM in an end-to-end (E2E) manner in CCF due to its multi-modal
nature: optimization involves redundant content encoding for interactions from
users. For this, we propose GRAM (GRadient Accumulation for Multi-modality):
(1) Single-step GRAM which aggregates gradients for each item while …

arxiv collaborative collaborative filtering fine-tuning language language models