Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning Recommendation Model for Personalization and
  Recommendation Systems   With the advent of deep learning, neural network-based recommendation models
have emerged as an important tool for tackling personalization and
recommendation tasks. These networks differ significantly from other deep
learning networks due to their need to handle categorical features and are not
well studied or understood. In this paper, we develop a state-of-the-art deep
learning recommendation model (DLRM) and provide its implementation in both
PyTorch and Caffe2 frameworks. In addition, we design a specialized
parallelization scheme utilizing model parallelism on the embedding tables to
mitigate memory constraints while exploiting data parallelism to scale-out
compute from the fully-connected layers. We compare DLRM against existing
recommendation models and characterize its performance on the Big Basin AI
platform, demonstrating its usefulness as a benchmark for future algorithmic
experimentation and system co-design.

9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
2
The categories of this paper are: math
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol