  Sequential Recommendation characterizes the evolving patterns by modeling
item sequences chronologically. The essential target of it is to capture the
item transition correlations. The recent developments of transformer inspire
the community to design effective sequence encoders, \textit{e.g.,} SASRec and
BERT4Rec. However, we observe that these transformer-based models suffer from
the cold-start issue, \textit{i.e.,} performing poorly for short sequences.
Therefore, we propose to augment short sequences while still preserving
original sequential correlations. We introduce a new framework for
\textbf{A}ugmenting \textbf{S}equential \textbf{Re}commendation with
\textbf{P}seudo-prior items~(ASReP). We firstly pre-train a transformer with
sequences in a reverse direction to predict prior items. Then, we use this
transformer to generate fabricated historical items at the beginning of short
sequences. Finally, we fine-tune the transformer using these augmented
sequences from the time order to predict the next item. Experiments on two
real-world datasets verify the effectiveness of ASReP. The code is available on
\url{https://github.com/DyGRec/ASReP}.
