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• W2079853600 abstract "Recommender systems are powerful tools that support the user in their quest to find the multimedia they are looking for. Such systems present multimedia contents or provide recommendations by taking into consideration two dimensions of inputs: content (item), and user (consumer). Little attention has been paid to increasing the quality of the experience by understanding the contextual aspect of the user when he/she wants to consume multimedia content. By including user's biological signal and leveraging collaborative filtering, we can build a context-aware model that establish the bridge between the multimedia content, and the user's context containing physiological parameters. Hence, the proposed model finds the latent preferences of users in a given context from other similar users. The model also finds the latent items consumed in a given context from other similar items. We then map context-based items for a particular user to find most relevant items in that context. Our experimental results have shown the feasibility to personalize the recommendation according to the user's context." @default.
• W2079853600 created "2016-06-24" @default.
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• W2079853600 date "2013-10-22" @default.
• W2079853600 modified "2023-09-22" @default.
• W2079853600 title "Leveraging biosignal and collaborative filtering for context-aware recommendation" @default.
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• W2079853600 doi "https://doi.org/10.1145/2505323.2505332" @default.
• W2079853600 hasPublicationYear "2013" @default.
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