  Predicting the popularity of online content is a fundamental problem in
various applications. One practical challenge takes roots in the varying length
of observation time or prediction horizon, i.e., a good model for popularity
prediction is desired to handle various prediction settings. However, most
existing methods adopt a separate training paradigm for each prediction setting
and the obtained model for one setting is difficult to be generalized to
others, causing a great waste of computational resources and a large demand for
downstream labels. To solve the above issues, we propose a novel pre-training
framework for popularity prediction, namely PREP, aiming to pre-train a general
representation model from the readily available unlabeled diffusion data, which
can be effectively transferred into various prediction settings. We design a
novel pretext task for pre-training, i.e., temporal elapse inference for two
randomly sampled time slices of popularity dynamics, impelling the
representation model to learn intrinsic knowledge about popularity dynamics.
Experimental results conducted on two real datasets demonstrate the
generalization and efficiency of the pre-training framework for different
popularity prediction task settings.
