  Training recommendation models on large datasets often requires significant
time and computational resources. Consequently, an emergent imperative has
arisen to construct informative, smaller-scale datasets for efficiently
training. Dataset compression techniques explored in other domains show
potential possibility to address this problem, via sampling a subset or
synthesizing a small dataset. However, applying existing approaches to condense
recommendation datasets is impractical due to following challenges: (i)
sampling-based methods are inadequate in addressing the long-tailed
distribution problem; (ii) synthesizing-based methods are not applicable due to
discreteness of interactions and large size of recommendation datasets; (iii)
neither of them fail to address the specific issue in recommendation of false
negative items, where items with potential user interest are incorrectly
sampled as negatives owing to insufficient exposure.
  To bridge this gap, we investigate dataset condensation for recommendation,
where discrete interactions are continualized with probabilistic
re-parameterization. To avoid catastrophically expensive computations, we adopt
a one-step update strategy for inner model training and introducing policy
gradient estimation for outer dataset synthesis. To mitigate amplification of
long-tailed problem, we compensate long-tailed users in the condensed dataset.
Furthermore, we propose to utilize a proxy model to identify false negative
items. Theoretical analysis regarding the convergence property is provided.
Extensive experiments on multiple datasets demonstrate the efficacy of our
method. In particular, we reduce the dataset size by 75% while approximating
over 98% of the original performance on Dianping and over 90% on other
datasets.
