  Given a sequence of sets, where each set has a timestamp and contains an
arbitrary number of elements, temporal sets prediction aims to predict the
elements in the subsequent set. Previous studies for temporal sets prediction
mainly focus on the modelling of elements and implicitly represent each user's
preference based on his/her interacted elements. However, user preferences are
often continuously evolving and the evolutionary trend cannot be fully captured
with the indirect learning paradigm of user preferences. To this end, we
propose a continuous-time user preference modelling framework for temporal sets
prediction, which explicitly models the evolving preference of each user by
maintaining a memory bank to store the states of all the users and elements.
Specifically, we first construct a universal sequence by arranging all the
user-set interactions in a non-descending temporal order, and then
chronologically learn from each user-set interaction. For each interaction, we
continuously update the memories of the related user and elements based on
their currently encoded messages and past memories. Moreover, we present a
personalized user behavior learning module to discover user-specific
characteristics based on each user's historical sequence, which aggregates the
previously interacted elements from dual perspectives according to the user and
elements. Finally, we develop a set-batch algorithm to improve the model
efficiency, which can create time-consistent batches in advance and achieve
3.5x and 3.0x speedups in the training and evaluation process on average.
Experiments on four real-world datasets demonstrate the superiority of our
approach over state-of-the-arts under both transductive and inductive settings.
The good interpretability of our method is also shown.
