  Existing sequential recommendation methods rely on large amounts of training
data and usually suffer from the data sparsity problem. To tackle this, the
pre-training mechanism has been widely adopted, which attempts to leverage
large-scale data to perform self-supervised learning and transfer the
pre-trained parameters to downstream tasks. However, previous pre-trained
models for recommendation focus on leverage universal sequence patterns from
user behaviour sequences and item information, whereas ignore capturing
personalized interests with the heterogeneous user information, which has been
shown effective in contributing to personalized recommendation. In this paper,
we propose a method to enhance pre-trained models with heterogeneous user
information, called User-aware Pre-training for Recommendation (UPRec).
Specifically, UPRec leverages the user attributes andstructured social graphs
to construct self-supervised objectives in the pre-training stage and proposes
two user-aware pre-training tasks. Comprehensive experimental results on
several real-world large-scale recommendation datasets demonstrate that UPRec
can effectively integrate user information into pre-trained models and thus
provide more appropriate recommendations for users.
