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 »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 data sets demonstrated that Caser consistently outperforms state-of-the-art sequential recommendation methods on a variety of common evaluation metrics.
Keywords
Computer scienceSequence (biology)EmbeddingRecommender systemVariety (cybernetics)Order (exchange)Sequential Pattern MiningArtificial intelligenceConvolutional neural networkInformation retrievalData mining
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Publication Info
- Year
- 2018
- Type
- preprint
- Pages
- 565-573
- Citations
- 1772
- Access
- Closed
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Cite This
Jiaxi Tang,
Ke Wang
(2018).
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding.
Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining
, 565-573.
https://doi.org/10.1145/3159652.3159656
Identifiers
- DOI
- 10.1145/3159652.3159656
- arXiv
- 1809.07426