Tencent Technology (shenzhen) Company Limited

Huawei Technologies Co., Limited

Cerence Operating Company

Feng Xia

Leyu Lin

Zhe Feng

Rui Wang

Yalong Wang

Lei Chen

Qian Wang

Yi Liu

Nan Li

Feng Liu

Bo Zhang

Lixin Zhang

Xu Zhang

Gang Zheng

Zhiping Wang

Junhong Yan

Yang Cui

Jun Rao

Su Yan

Zhijie Qiu

Chunhua Tan

Junwei Zhong

Zhiwei Guo

Ruobing Xie

Shukai Liu

Zhenlong Sun

Ming Zou

Yang Zuo

Liangdong Wang

Jianxiong Feng

Xiumin Lin

Ge Wang

Jiayi Ding

Bruno Xu

Lei Shiah

Yafei Zhang

Lihuan Du

Jianchao Lin

Cheng Ling

Chien-Pang Lin

David Yu

Shenbin Zhao

Lantao Hu

Xiangcong Zeng

Mu Yuan

Xulin Liao

Shanpeng Sun

Chenglin Zhong

Qihao Zhu

A system and method for providing avatar device status indicators for voice assistants in multi-zone vehicles. The method comprises: receiving at least one signal from a plurality of microphones, wherein each microphone is associated with one of a plurality of spatial zones, and one of a plurality of avatar devices; wherein the at least one signal further comprises a speech signal component from a speaker; wherein the speech signal component is a voice command or question; sending zone information associated with the speaker and with one of the plurality of spatial zones to an avatar; activating one the plurality of avatar devices in a respective one of the plurality of spatial zones associated with the speaker.

Vehicle avatar devices for interactive virtual assistant

This application provides a feature processing method and apparatus for an artificial intelligence (AI) recommendation model, an electronic device, and a storage medium. The method includes obtaining input data, and converting a data structure of the input data into a uniform sample structure; determining a feature group identifier and a feature extraction function that correspond to the AI recommendation model; extracting a feature group identifier, a feature identifier and a feature value of the input data from the converted input data according to the feature extraction function; and constructing a sample of the AI recommendation model according to the feature group identifier, the feature identifier and the feature value of the input data.

Feature processing method and apparatus for artificial intelligence recommendation model, electronic device, and storage medium

Embodiments of the present disclosure relate to a content processing method and apparatus, a computer-readable storage medium, and a computer device. The method includes obtaining data of a to-be-pushed user in behavior evaluation dimensions, user portrait information of the to-be-pushed user, and to-be-pushed content, with the user portrait information corresponding to a user portrait code and the to-be-pushed content corresponding to a to-be-pushed content code. The method further includes determining a user behavior code based on the data of the to-be-pushed user in the behavior evaluation dimensions, and obtaining a target code by fusing the to-be-pushed content code with the user behavior code and the user portrait code. Then, the method includes determining a recommendation probability of the to-be-pushed content based on the target code to determine the push content based on the recommendation probability.

Content processing method and apparatus, computer-readable storage medium and computer device

A cache space management method and apparatus are disclosed. In the method, first, a hit rate of the read cache of the storage system is obtained; and then, a size of the read cache and a size of the metadata cache are adjusted based on the hit rate of the read cache. In the foregoing technical solution, the size of the read cache and the size of the metadata cache are dynamically adjusted by using the hit rate of the read cache as a decision factor. For example, when the hit rate of the read cache is relatively high, the size of the read cache may be increased.

Cache space management method and apparatus

This application provides a content recommendation method and apparatus, an electronic device, and a storage medium. The content recommendation method includes obtaining content feedback information of a target object and content feature information of content that is to be recommended in response to a content recommendation request of the target object, the content feedback information comprising explicit feedback information and implicit feedback information and object portrait information of the target object; performing feature interaction according to the explicit feedback information and the implicit feedback information in the content feedback information, and obtaining behavior preference information; performing feature extraction based on the behavior preference information, the content feedback information, and the content feature information, and obtaining a predicted click-through rate (CTR); and determining, according to the predicted CTR, recommended content from the pieces of content that is to be recommended, and transmitting the recommended content to a terminal device.

Content recommendation method and apparatus, electronic device, and storage medium

This application provides an article recommendation method and apparatus, a computer device, and a storage medium. The method includes: obtaining a historical reading article set of a to-be-analyzed target user, the historical reading article set comprising a plurality of historical articles that have been read by the target user at different reading moments; determining, for each historical article of the plurality of historical articles, first attention degree values between the each historical article and candidate reference historical articles having reading moments before that of the each historical article, the first attention degree values reflecting a possibility that a user is recommended to read the historical article if the user has read the candidate reference historical articles; selecting at least one recommendation reference article from the candidate reference historical articles corresponding to the historical articles according to the first attention degree values between the historical articles and corresponding candidate reference historical articles; and determining, in a recommendable article set at least one candidate recommended article to be recommended to the target user based on the at least one recommendation reference article.

Article recommendation method and apparatus, computer device, and storage medium

Embodiments of the present invention disclose a result ranking method and device. The method includes: acquiring search key-information by using an interaction application, and searching a prestored interaction data set for at least one search result associated with the search key-information; ranking the at least one search result according to a quality assessment score corresponding to each of the at least one search result; and outputting the ranked at least one search result. The quality assessment score corresponding to each search result is a value generated according to a number of historical operations performed on each search result and an interactive influence score of an application identifier performing a historical operation on each search result.

Search result ranking method and device

This application discloses an information recommendation method and apparatus, a device, and a storage medium, and belongs to the field of information recommendation. The method includes starting an application program according to a start operation, a first account being logged in in the application program; obtaining a recommended information flow for the first account, recommended information in the recommended information flow including at least one piece of interactive recommended information, the interactive recommended information being information for which a second account generates an interactive message, the first account and the second account having a social relationship; and displaying an information presentation interface, the information presentation interface comprising the recommended information displayed in an information flow form.

Information recommendation method and apparatus, device, and storage medium

A method for migrating a file in a storage device. The storage device obtains a snapshot of metadata of the file. The snapshot includes logical addresses of data blocks of the file and first write time points of the data blocks. The storage device identifies a logical address of a target data block and a first write time point of the target data block. Then, the storage device obtains a second write time point from the metadata of the file. The second write time point indicates a latest time point of writing the target data block into the storage device. If the first write time point of the target data block is identical to the second write time point, the storage device migrates the target data block to pre-allocated continuous storage space. Thereby, disk defragmentation can be achieved.

File migration method and apparatus, and storage device

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Shenzhen, China

Feng Xia