Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Lianghui Chen

Yan Fu

Jun Fang

Yangjie Zhou

Huiqiang Zhong

Siqi Xu

Shunnan Xu

Quanbin Wang

Liangang Peng

Xiaoxuan Yang

Lu Gan

Chenhui Liu

A method includes: sending, by one or more computers, in response to the number of data providers for federated learning being greater than a first threshold, a data field required for the federated learning to a coordinator, the coordinator comprising a computer; receiving, by one or more computers, from the coordinator, information about one or more data providers comprising the required data field, for determining the data providers comprising the required data field as the remaining data providers, wherein the coordinator stores a data field of each data provider; and performing, by one or more computers, federated learning-based modeling with each of the remaining data providers.

Federated learning for improving matching efficiency

A method, electronic device and storage medium for item recommendation and for model training, which relates to the field of artificial intelligence, are disclosed. According to some embodiments: an item feature expression database is created using a pre-trained user-clicking-item task model and at least two pieces of feature information of items in the repository of items to be recommended; a feature expression of a user is obtained using the pre-trained user-clicking-item task model and at least two pieces of feature information of the user; identifiers of N items to be recommended are obtained according to the feature expression of the user and the item feature expression database; relevant information of the N items is recommended to the user based on the identifiers of the N items.

Method, electronic device and storage medium for item recommendation

A method, an apparatus, a device and a storage medium for embedding user app interest are provided. The method includes: acquiring a user existing app installation list and a user app installation list within a predetermined time window, where the app includes app ID information and app category information; inputting the existing app installation list and the app installation list within the predetermined time window into a pre-trained user app interest embedding model to obtain a user app interest embedding vector. By combining the user existing app installation list information and the user recent app installation list information, the user app interest embedding vector may simultaneously reflect the user long-term interest and the user short-term interest.

Method, apparatus, device and storage medium for embedding user app interest

The disclosure provides an image clustering method and an image clustering apparatus. The method includes: obtaining new images, and clustering the new images to obtain a first cluster; determining a historical cluster similar to the first cluster as a second cluster from existing historical clusters; obtaining a distance between the first cluster and the second cluster; and generating a target cluster by fusing the first cluster and the second cluster based on the distance. In the image clustering method, with the image clustering apparatus of the disclosure, secondary clustering processing performed on the existing historical clusters based on newly added images is not required, new and old clusters are directly fused.

Image clustering method and apparatus, and storage medium

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Beijing, China

Lianghui Chen