  Recent sequential recommendation models rely increasingly on consecutive
short-term user-item interaction sequences to model user interests. These
approaches have, however, raised concerns about both short- and long-term
interests. (1) {\it short-term}: interaction sequences may not result from a
monolithic interest, but rather from several intertwined interests, even within
a short period of time, resulting in their failures to model skip behaviors;
(2) {\it long-term}: interaction sequences are primarily observed sparsely at
discrete intervals, other than consecutively over the long run. This renders
difficulty in inferring long-term interests, since only discrete interest
representations can be derived, without taking into account interest dynamics
across sequences. In this study, we address these concerns by learning (1)
multi-scale representations of short-term interests; and (2) dynamics-aware
representations of long-term interests. To this end, we present an
\textbf{I}nterest \textbf{D}ynamics modeling framework using generative
\textbf{N}eural \textbf{P}rocesses, coined IDNP, to model user interests from a
functional perspective. IDNP learns a global interest function family to define
each user's long-term interest as a function instantiation, manifesting
interest dynamics through function continuity. Specifically, IDNP first encodes
each user's short-term interactions into multi-scale representations, which are
then summarized as user context. By combining latent global interest with user
context, IDNP then reconstructs long-term user interest functions and predicts
interactions at upcoming query timestep. Moreover, IDNP can model such interest
functions even when interaction sequences are limited and non-consecutive.
Extensive experiments on four real-world datasets demonstrate that our model
outperforms state-of-the-arts on various evaluation metrics.
