Abstract: The present disclosure provides a method and an apparatus of recommending information based on a fused relationship network. The method includes: determining association relationships between an any node and other nodes in the fused relationship network based on at least one of a weight, interaction information and data source information of the interaction information of an edge; and recommending information to a user represented by the any node based on the association relationships. The present disclosure further provides an apparatus of recommending information based on a fused relationship network, and an electronic device and a storage medium.

Abstract: A relationship network generation method and device, electronic apparatus, and a storage medium are provided, which are related to big data processing. In an implementation, at least one historical text data corresponding to N users within a preset duration is acquired, where N is an integer greater than or equal to 1; sampling is performed on at least one historical text data corresponding to the N users to obtain the sampled text data respectively corresponding to the N users; semantic vectors corresponding to the N users respectively are determined based on the sampled text data corresponding to the N users respectively, and a semantic relationship network involving the N users is generated based on the semantic vectors corresponding to the N users respectively.

Abstract: A network convergence method and device, an electronic apparatus, a storage medium are provided. The method includes: taking online information obtained at least from network data as first nodes, and constructing a first network with the first nodes based on an association among the online information, the online information including at least one account and at least one piece of network media information; taking offline information obtained at least from mobile communication data as second nodes, and constructing a second network with the second nodes based on an association among the offline information, the offline information including at least one terminal identifier and location information of a terminal corresponding to the at least one terminal identifier; and converging the first network and the second network by using an association between the account in the online information and the terminal identifier in the offline information, to construct a heterogeneous network.

Abstract: The present disclosure provides a method and an apparatus of recommending information based on a fused relationship network. The method includes: determining association relationships between an any node and other nodes in the fused relationship network based on at least one of a weight, interaction information and data source information of the interaction information of an edge; and recommending information to a user represented by the any node based on the association relationships. The present disclosure further provides an apparatus of recommending information based on a fused relationship network, and an electronic device and a storage medium.

Abstract: The present disclosure provides a method of constructing a fused relationship network. The method includes: obtaining interaction data from a plurality of data sources, wherein the interaction data contains a plurality of user relationship information; generating, based on the identification information for each user in each user relationship information, a node for the user in the fused relationship network, and generating, based on the interaction information between two users in each user relationship information, an edge between the nodes for the two users in the fused relationship network, wherein a same user identification is generated as one node. The present disclosure further provides an apparatus of constructing a fused relationship network, an electronic device, and a storage medium.

Abstract: A relationship network generation method and device, electronic apparatus, and a storage medium are provided, which are related to big data processing. In an implementation, at least one historical text data corresponding to N users within a preset duration is acquired, where N is an integer greater than or equal to 1; sampling is performed on at least one historical text data corresponding to the N users to obtain the sampled text data respectively corresponding to the N users; semantic vectors corresponding to the N users respectively are determined based on the sampled text data corresponding to the N users respectively, and a semantic relationship network involving the N users is generated based on the semantic vectors corresponding to the N users respectively.

Abstract: The present application discloses a method for generating a user interactive information processing model and a method for processing user interactive information, relates to the technical field of graph neural networks, and particularly relates to a user interactive information processing technology. The method comprises the following steps: determining node representations of network nodes of each layer in a graph neural network in an iterative manner, wherein the node representation of a single-layer node is obtained by representations of neighbor nodes of the single-layer node; adding the attribute feature of each node in each layer of network nodes to the node representation of each layer of network nodes to obtain a graph neural network to be trained; and training the graph neural network to be trained based on existing user interactive information, to obtain a user interactive information processing model, wherein the user interactive information processing model comprises adjusted node representation.

Abstract: The present disclosure relates to data processing systems and data processing methods. One example data processing system includes a first computing node, the first computing node includes an artificial intelligence (AI) processor and a reducing operator, the AI processor is configured to perform an AI operation to generate first data of the first computing node, and the reducing operator is configured to perform a reducing operation on second data from a second computing node and the first data to generate a reducing operation result.

Abstract: A network convergence method and device, an electronic apparatus, a storage medium are provided. The method includes: taking online information obtained at least from network data as first nodes, and constructing a first network with the first nodes based on an association among the online information, the online information including at least one account and at least one piece of network media information; taking offline information obtained at least from mobile communication data as second nodes, and constructing a second network with the second nodes based on an association among the offline information, the offline information including at least one terminal identifier and location information of a terminal corresponding to the at least one terminal identifier; and converging the first network and the second network by using an association between the account in the online information and the terminal identifier in the offline information, to construct a heterogeneous network.

Abstract: A tree topology based computing system and method, where the system may include a plurality of node clusters, where the plurality of node clusters constitute a multi-layer network structure in a tree topology manner, any minimum tree in the network structure includes a second node cluster and at least one first node cluster. The first node cluster is configured to obtain a first computing result based on a first computing input, and send the first computing result to the second node cluster, and the second node cluster is configured to receive at least one first computing result sent by the at least one first node cluster, and aggregate the at least one first computing result and a second computing result to obtain a third computing result.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for outputting information. A specific embodiment of the method includes: acquiring at least one personal attribute characteristic of a target user; determining, based on the acquired at least one personal attribute characteristic, a user type of the target user under a preset attribute; and outputting the determined user type. This embodiment effectively utilizes the personal attribute characteristic of the user to predict the user type of the user under the preset attribute, and improves the content richness of the information output.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for outputting information.