Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding

TL;DR

This paper introduces Caser, a convolutional model that embeds user interaction sequences as images to effectively capture sequential patterns for improved top-N recommendations.

Contribution

The paper proposes a novel convolutional sequence embedding model (Caser) that captures both general preferences and sequential patterns in user interaction data.

Findings

Caser outperforms state-of-the-art methods on public datasets.

Embedding sequences as images enhances pattern recognition.

The model effectively balances preference and sequential information.

Abstract

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.