  Sequential recommendation aims to model dynamic user behavior from historical
interactions. Self-attentive methods have proven effective at capturing
short-term dynamics and long-term preferences. Despite their success, these
approaches still struggle to model sparse data, on which they struggle to learn
high-quality item representations. We propose to model user dynamics from
shopping intents and interacted items simultaneously. The learned intents are
coarse-grained and work as prior knowledge for item recommendation. To this
end, we present a coarse-to-fine self-attention framework, namely CaFe, which
explicitly learns coarse-grained and fine-grained sequential dynamics.
Specifically, CaFe first learns intents from coarse-grained sequences which are
dense and hence provide high-quality user intent representations. Then, CaFe
fuses intent representations into item encoder outputs to obtain improved item
representations. Finally, we infer recommended items based on representations
of items and corresponding intents. Experiments on sparse datasets show that
CaFe outperforms state-of-the-art self-attentive recommenders by 44.03% NDCG@5
on average.
