  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.
