Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
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Machine scores (provisional)
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- Teacher spread
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- Validation status
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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.
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The record
- Venue
- Topic
- Recommender Systems and Techniques
- Field
- Computer Science
- Canadian institutions
- Simon Fraser University
- Funders
- —
- Keywords
- Computer scienceSequence (biology)EmbeddingRecommender systemVariety (cybernetics)Order (exchange)Sequential Pattern MiningArtificial intelligenceConvolutional neural networkInformation retrievalData mining
- Has abstract in OpenAlex
- yes