Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GRAM: Fast Fine-tuning of Pre-trained Language Models for Content-based
  Collaborative Filtering   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 maintaining
theoretical equivalence with E2E, and (2) Multi-step GRAM which further
accumulates gradients across multiple training steps, with less than 40\% GPU
memory footprint of E2E. We empirically confirm that GRAM achieves a remarkable
boost in training efficiency based on five datasets from two task domains of
Knowledge Tracing and News Recommendation, where single-step and multi-step
GRAM achieve 4x and 45x training speedup on average, respectively.

18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
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The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol