Abstract: This application provides a content recommendation method and apparatus, an electronic device, and a storage medium.

Abstract: Embodiments of this application disclose a data recommendation method performed by a computer device. The data recommendation method includes: obtaining a service object set associated with a target user in a plurality of fields in response to a data recommendation request for the target user in a target field, performing cross-field cross-encoding on the plurality of fields and the service object set to obtain a target field interest feature of the target user in the target field; obtaining to-be-recommended service object features of a plurality of to-be-recommended service objects in the target field; and obtaining a target to-be-recommended service object feature matching with the target field interest feature of the target user from the to-be-recommended service object features, and returning a target to-be-recommended service object corresponding to the target to-be-recommended service object feature to the target user.

Abstract: Embodiments of this application provide a for training an information recommendation model. The method includes: obtaining historical user behavior data in a plurality of product domains; generating candidate sample data of one or more target product domains according to the historical user behavior data by using a generative model; performing user-specific authenticity sample discrimination on candidate sample data of the target product domains and actual user click sample data by using a discriminative model, to obtain a discrimination result; and performing adversarial training on the generative model and the discriminative model according to the discrimination result, to obtain a trained generative adversarial network as an information recommendation model for a to-be-expanded product domain in the plurality of product domains.

Abstract: This application discloses a resource pushing method performed by a computer device. The method includes: obtaining a target recommendation model and a preference feature and a candidate resource set corresponding to a target object, the preference feature including at least a channel preference feature and a content preference feature; obtaining at least one target resource from the candidate resource set based on the target recommendation model and the preference feature; and pushing the at least one target resource to the target object. Such a resource pushing process integrates preferences of the target object in different dimensions, so that the target resource pushed to the target object not only conforms to channel preferences of the target object, but also conforms to content references of the target object, which is beneficial to improving the resource pushing effect, and further increasing the click-through rates (CTRs) of the pushed resources.

Abstract: This application provides a content recommendation method and apparatus, an electronic device, and a storage medium.

Abstract: The present disclosure provides a data processing method performed by an electronic device. The method includes: obtaining first and second service behavior features, and a service scenario feature of an object from service history data associated with the object; generating first and second service embedding vectors and a scenario representation vector according to the first and second service behavior features and the service scenario feature; obtaining first and second weights of the first and second service embedding vectors according to the scenario representation vector and the first and second service embedding vectors; generating first and second service feature vectors according to the first and second service embedding vectors and the first and second weights of the first and second service embedding vectors, respectively; and obtaining an object embedding vector according to the first service feature vector and the second service feature vector.