  Top-$N$ sequential recommendation models each user as a sequence of items
interacted in the past and aims to predict top-$N$ ranked items that a user
will likely interact in a `near future'. The order of interaction implies that
sequential patterns play an important role where more recent items in a
sequence have a larger impact on the next item. In this paper, we propose a
Convolutional Sequence Embedding Recommendation Model (\emph{Caser}) as a
solution to address this requirement. The idea is to embed a sequence of recent
items into an `image' in the time and latent spaces and learn sequential
patterns as local features of the image using convolutional filters. This
approach provides a unified and flexible network structure for capturing both
general preferences and sequential patterns. The experiments on public datasets
demonstrated that Caser consistently outperforms state-of-the-art sequential
recommendation methods on a variety of common evaluation metrics.
