METHOD FOR PROVIDING RECOMMENDED CONTENT LIST AND ELECTRONIC DEVICE ACCORDING THERETO

    公开(公告)号:US20220147870A1

    公开(公告)日:2022-05-12

    申请号:US17421292

    申请日:2020-01-06

    Abstract: An electronic device according to an embodiment of the disclosure includes: a communicator; a memory storing one or more instructions; at least one processor configured to execute the one or more instructions stored in the memory to collect content metadata and user metadata from a plurality of different servers that provide content, obtain a content latent factor including information about similarities between pieces of the content based on characteristics of the content metadata, by using a first learning network model, obtain a user latent factor related to user preferred content information based on characteristics of the user metadata, by using a second learning network model, obtain a user preference score for the content based on the content latent factor and the user latent factor, by using a third learning network model, and provide a recommended content list based on the user preference score.

    COMPUTING DEVICE AND OPERATING METHOD THEREOF

    公开(公告)号:US20230362444A1

    公开(公告)日:2023-11-09

    申请号:US17312295

    申请日:2021-01-08

    CPC classification number: H04N21/4666 H04N21/4667

    Abstract: The disclosure relates to an artificial intelligence (AI) system and an application thereof. A computing device disclosed includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory, wherein the processor is configured to execute the one or more instructions to input a recommendation recipient's consumption information to a first neural network configured to receive item consumption information of a user and reconstruct the item consumption information and a second neural network embedded into the first neural network and having been trained with respect to metadata consumption information corresponding to the item consumption information, and obtain item recommendation information to which the metadata consumption information is reflected.

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