Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
This work addresses the need for accurate sequential recommendations in domains like e-commerce, though it appears incremental as it builds on existing methods with a novel convolutional approach.
The paper tackled the problem of personalized top-N sequential recommendation by modeling user interactions as sequences and predicting future items, with the proposed Caser model outperforming state-of-the-art methods on public datasets across multiple metrics.
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.