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 (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 lters. This approach provides a uni ed and exible network structure for capturing both general preferences and sequential patterns. The experiments on public data sets demonstrated that Caser consistently outperforms state-of-the-art sequential recommendation methods on a variety of common evaluation metrics.
REF presents a novel solution to top-N sequential recommendation by providing an united and flexible network for capturing more features.
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
WSDM '18
Computer Science