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• W2016420487 abstract "For recommender systems, time is often an important source of information but it is also a complex dimension to apprehend. We propose here to learn item and user representations such that any timely ordered sequence of items selected by a user will be represented as a trajectory of the user in a representation space. This allows us to rank new items for this user. We then enrich the item and user representations in order to perform rating prediction using a classical matrix factorization scheme. We demonstrate the interest of our approach regarding both item ranking and rating prediction on a series of classical benchmarks." @default.
• W2016420487 created "2016-06-24" @default.
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• W2016420487 date "2015-09-16" @default.
• W2016420487 modified "2023-09-25" @default.
• W2016420487 title "Latent Trajectory Modeling" @default.
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• W2016420487 doi "https://doi.org/10.1145/2792838.2799676" @default.
• W2016420487 hasPublicationYear "2015" @default.
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