Method and apparatus for pushing information based on artificial intelligence

    公开(公告)号:US11349680B2

    公开(公告)日:2022-05-31

    申请号:US16054812

    申请日:2018-08-03

    Abstract: Embodiments of the disclosure disclose a method and apparatus for pushing information based on artificial intelligence. A specific embodiment of the method includes: mining, in response to a new match occurring, real-time match data of the match and real-time associated data of the match; generating structured data using the real-time match data; generating a to-be-recommended item using the real-time associated data and an offline item; determining whether a current time point is a recommendation node based on the structured data and real-time match state information acquired from a state manager; generating a to-be-pushed message based on the to-be-recommended item, the real-time match state information, and basic match information acquired from the state manager, and updating a to-be-pushed message record in the state manager, if the current time point is the recommendation node; and pushing the to-be-pushed message. The embodiment has improved the quality and timeliness of pushing a to-be-pushed message.

    Language generation method and apparatus, electronic device and storage medium

    公开(公告)号:US11562150B2

    公开(公告)日:2023-01-24

    申请号:US17031569

    申请日:2020-09-24

    Abstract: The present disclosure proposes a language generation method and apparatus. The method includes: performing encoding processing on an input sequence by using a preset encoder to generate a hidden state vector corresponding to the input sequence; in response to a granularity category of a second target segment being a phrase, decoding a first target segment vector, the hidden state vector, and a position vector corresponding to the second target segment by using N decoders to generate N second target segments; determining a loss value based on differences between respective N second target segments and a second target annotated segment; and performing parameter updating on the preset encoder, a preset classifier, and the N decoders based on the loss value to generate an updated language generation model for performing language generation.

    Topic Subscription Method and Apparatus, and Storage Medium

    公开(公告)号:US20190155856A1

    公开(公告)日:2019-05-23

    申请号:US16234337

    申请日:2018-12-27

    Abstract: Embodiments of the present disclosure disclose a method and apparatus for subscribing to a topic. The method includes: matching a persistent topic for a retrieval keyword based on the retrieval keyword of a user combined with at least one of historical behavior or a subscription record of the user; returning the persistent topic to a client for display, so that the user performs subscription; and saving the persistent topic subscribed to by the user, and when a matching resource corresponding to the persistent topic subscribed to by the user is updated, recommending the updated matching resource to the user. Since a recommendation strategy can be timely adjusted according to real-time behavior of a user combined with historical behavior, the probability of hitting a topic of interest to the user is increased.

    Multi-lingual model training method, apparatus, electronic device and readable storage medium

    公开(公告)号:US11995405B2

    公开(公告)日:2024-05-28

    申请号:US17348104

    申请日:2021-06-15

    CPC classification number: G06F40/30 G06F40/58 G06N20/00

    Abstract: The present disclosure provides a multi-lingual model training method, apparatus, electronic device and readable storage medium and relates to the technical field of deep learning and natural language processing. A technical solution of the present disclosure when training the multi-lingual model is: obtaining training corpuses comprising a plurality of bilingual corpuses and a plurality of monolingual corpuses; training a multi-lingual model with a first training task by using the plurality of bilingual corpuses; training the multi-lingual model with a second training task by using the plurality of monolingual corpuses; and completing the training of the multi-lingual model in a case of determining that loss functions of the first training task and second training task converge. In the present disclosure, the multi-lingual model can be enabled to achieve semantic interaction between different languages and improve the accuracy of the multi-lingual model in learning the semantic representations of the multi-lingual model.

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