  Sequential recommenders are crucial to the success of online applications,
\eg e-commerce, video streaming, and social media. While model architectures
continue to improve, for every new application domain, we still have to train a
new model from scratch for high quality recommendations. On the other hand,
pre-trained language and vision models have shown great success in zero-shot or
few-shot adaptation to new application domains. Inspired by the success of
pre-trained models in peer AI fields, we propose a novel pre-trained sequential
recommendation framework: PrepRec. We learn universal item representations by
modeling item popularity dynamics. Through extensive experiments on five
real-world datasets, we show that PrepRec, without any auxiliary information,
can not only zero-shot transfer to a new domain, but achieve competitive
performance compared to state-of-the-art sequential recommender models with
only a fraction of the model size. In addition, with a simple post-hoc
interpolation, PrepRec can improve the performance of existing sequential
recommenders on average by 13.8\% in Recall@10 and 29.5% in NDCG@10. We provide
an anonymized implementation of PrepRec at
https://anonymous.4open.science/r/PrepRec--2F60/
