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

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

Abstract: A thermoplastic component may include a matrix component (A) and a filler component (B). The matrix component (A) may include a polyetheretherketone and the filler component (B) may include inorganic particles and carbon-containing particles. The overall composition may include equal or different portions, as filler component (B), of hydrophobic silicon dioxide, carbon fibres, titanium dioxide particles, graphite particles, and a particulate lubricant selected from divalent metallic sulfides and alkaline earth metal sulfates.

Abstract: A recommendation method includes obtaining a candidate item to be recommended to a social network user, the social network user belonging to a group of users in an online social network, and determining attention of the group to the candidate item in the online social network. The determined attention of the group to the candidate item is based on an importance weight of each user in the group and based on attention of each user in the group to the candidate item. The method further includes determining, according to the determined attention of the group, whether to recommend the candidate item to the social network user through the online social network.

Abstract: A recommendation method is provided. In the method, a candidate item to be recommended to a social network user is obtained. The social network user has at least two different types of social relationships. For at least one target social object in each of the at least two different types of social relationships of the social network user, attention of each of the at least one target social object in the respective type of social relationship to the candidate item is determined. According to the attention of each of the at least one target social object in the at least two different types of social relationships to the candidate item, a comprehensive attention of the target social objects of the at least two different types of social relationships to the candidate item is determined. According to the comprehensive attention, whether to recommend the candidate item to the social network user is determined.

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: 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.

Abstract: A method for obtaining a user portrait includes: obtaining a user feature vector of a target user and tag feature vectors of content tags of multimedia content in a target application, and determining an alternative tag of the target user according to similarities between the user feature vector and the tag feature vectors, to further determine a user portrait of the target user according to the alternative tag.

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: 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.

Abstract: 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.

Abstract: This application provides a method for constructing a network structure optimizer performed by an electronic device.

Abstract: This application discloses a content recommendation method performed at a computer device and belongs to the field of artificial intelligence. The method includes: acquiring a target user vector of a target user; determining n groups of seed user vectors according to the target user vector, each group of seed user vectors corresponding to a respective piece of candidate recommendation content; invoking a look-alike model to calculate a similarity between the target user vector and each group of seed user vectors, the look-alike model being used for calculating a similarity between user vectors based on an attention mechanism; and determining, among the n pieces of candidate recommendation content, target content to be recommended to the target user according to the respective similarities of the corresponding n groups of seed user vectors. This application can resolve a problem of relatively low accuracy of a recommendation method in the related art.

Abstract: A method is provided for determining a social rank of a node in a social network, the social network including a plurality of nodes connected by relationship chains. The method includes determining a user corresponding to at least one of the plurality of nodes in the social network, determining a connection structure of the relationship chains between the plurality of nodes, and determining, according to the connection structure of the relationship chains between the plurality of nodes, a social rank of the user corresponding to the at least one of the plurality of nodes.

Abstract: Embodiments of this application provide a method for making recommendations to a user and an apparatus, a computing device, and a storage medium. The method includes obtaining user attribute information, reading attribute information, reading history information, and candidate items; performing intra-group information fusion on the reading attribute information according to preset groupings to obtain reading feature information; obtaining a reading history weight according to the reading history information; obtaining history feature information according to the reading history weight and the reading history information; obtaining user feature information according to the user attribute information, the reading feature information, and the history feature information; and selecting a recommendation item from the candidate items according to the user feature information.

Abstract: 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.

Abstract: A recommendation method is provided. In the method, a candidate item to be recommended to a social network user is obtained. The social network user has at least two different types of social relationships. For at least one target social object in each of the at least two different types of social relationships of the social network user, attention of each of the at least one target social object in the respective type of social relationship to the candidate item is determined. According to the attention of each of the at least one target social object in the at least two different types of social relationships to the candidate item, a comprehensive attention of the target social objects of the at least two different types of social relationships to the candidate item is determined. According to the comprehensive attention, whether to recommend the candidate item to the social network user is determined.

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