  Large scale recommender models find most relevant items from huge catalogs,
and they play a critical role in modern search and recommendation systems. To
model the input space with large-vocab categorical features, a typical
recommender model learns a joint embedding space through neural networks for
both queries and items from user feedback data. However, with millions to
billions of items in the corpus, users tend to provide feedback for a very
small set of them, causing a power-law distribution. This makes the feedback
data for long-tail items extremely sparse.
  Inspired by the recent success in self-supervised representation learning
research in both computer vision and natural language understanding, we propose
a multi-task self-supervised learning (SSL) framework for large-scale item
recommendations. The framework is designed to tackle the label sparsity problem
by learning better latent relationship of item features. Specifically, SSL
improves item representation learning as well as serving as additional
regularization to improve generalization. Furthermore, we propose a novel data
augmentation method that utilizes feature correlations within the proposed
framework.
  We evaluate our framework using two real-world datasets with 500M and 1B
training examples respectively. Our results demonstrate the effectiveness of
SSL regularization and show its superior performance over the state-of-the-art
regularization techniques. We also have already launched the proposed
techniques to a web-scale commercial app-to-app recommendation system, with
significant improvements top-tier business metrics demonstrated in A/B
experiments on live traffic. Our online results also verify our hypothesis that
our framework indeed improves model performance even more on slices that lack
supervision.
