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• W2912298496 abstract "In this paper, we study the problem of recommending personalized items to users given their sequential behaviors. Most sequential recommendation models only capture a user's short-term preference in a short session, and neglect his general (unchanged over time) and long-term preferences. Besides, they are all based on deterministic neural networks, and consider users' latent preferences as point vectors in a low-dimensional continuous space. However, in real world, the evolutions of users' preferences are full of uncertainties. We address this problem by proposing a hierarchical neural variational model (HNVM). HNVM models users' three preferences: general, long-term and short-term preferences through an unified hierarchical deep generative process. HNVM is a hierarchical recurrent neural network that enables it to capture both user's long-term and short-term preferences. Experiments on two public datasets demonstrate that HNVM outperforms state-of-the-art sequential recommendation methods." @default.
• W2912298496 created "2019-02-21" @default.
• W2912298496 creator A5054039763 @default.
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• W2912298496 date "2019-05-13" @default.
• W2912298496 modified "2023-10-16" @default.
• W2912298496 title "Hierarchical Neural Variational Model for Personalized Sequential Recommendation" @default.
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• W2912298496 doi "https://doi.org/10.1145/3308558.3313603" @default.
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