  Modeling the evolution of user preference is essential in recommender
systems. Recently, dynamic graph-based methods have been studied and achieved
SOTA for recommendation, majority of which focus on user's stable long-term
preference. However, in real-world scenario, user's short-term preference
evolves over time dynamically. Although there exists sequential methods that
attempt to capture it, how to model the evolution of short-term preference with
dynamic graph-based methods has not been well-addressed yet. In particular: 1)
existing methods do not explicitly encode and capture the evolution of
short-term preference as sequential methods do; 2) simply using last few
interactions is not enough for modeling the changing trend. In this paper, we
propose Long Short-Term Preference Modeling for Continuous-Time Sequential
Recommendation (LSTSR) to capture the evolution of short-term preference under
dynamic graph. Specifically, we explicitly encode short-term preference and
optimize it via memory mechanism, which has three key operations: Message,
Aggregate and Update. Our memory mechanism can not only store one-hop
information, but also trigger with new interactions online. Extensive
experiments conducted on five public datasets show that LSTSR consistently
outperforms many state-of-the-art recommendation methods across various lines.
