  Deep learning recommendation models (DLRMs) are at the heart of the current
e-commerce industry. However, the amount of training data used to train these
large models is growing exponentially, leading to substantial training hurdles.
The training dataset contains two primary types of information: content-based
information (features of users and items) and collaborative information
(interactions between users and items). One approach to reduce the training
dataset is to remove user-item interactions. But that significantly diminishes
collaborative information, which is crucial for maintaining accuracy due to its
inclusion of interaction histories. This loss profoundly impacts DLRM
performance.
  This paper makes an important observation that if one can capture the
user-item interaction history to enrich the user and item embeddings, then the
interaction history can be compressed without losing model accuracy. Thus, this
work, Collaborative Aware Data Compression (CADC), takes a two-step approach to
training dataset compression. In the first step, we use matrix factorization of
the user-item interaction matrix to create a novel embedding representation for
both the users and items. Once the user and item embeddings are enriched by the
interaction history information the approach then applies uniform random
sampling of the training dataset to drastically reduce the training dataset
size while minimizing model accuracy drop. The source code of CADC is available
at
\href{https://anonymous.4open.science/r/DSS-RM-8C1D/README.md}{https://anonymous.4open.science/r/DSS-RM-8C1D/README.md}.
