Method and device for object feature information acquisition, object classification, and information push

Abstract

The embodiment of this specification provides a method and device for acquiring object characteristic information, classifying objects, and pushing information. When determining the feature information of the object, determine the multiple neighbor nodes of the first node from the N relational networks at N time points, and obtain N neighbor node groups, and based on the neighbor node group corresponding to each time point and the first node The node characteristics of the first node are determined at each moment of the spatial aggregation characteristics; the N spatial aggregation characteristics of the N moments are input into the sequence neural network in a sequential manner according to the time sequence, and the N spatial aggregation characteristics of the first node at the N moments are respectively obtained. Spatio-temporal expression: Aggregating the N spatio-temporal expressions to obtain the spatio-temporal aggregation feature of the first node, which is used as feature information of the first object represented by the first node.

Description

technical field [0001] One or more embodiments of this specification relate to the technical field of graph computing, and in particular to methods and devices for acquiring object feature information, classifying objects, and pushing information. Background technique [0002] In the era of big data, a large amount of object-relational data can be obtained, through which a relational network of multiple objects can be constructed, and nodes are used to represent objects. For example, a relational network for objects such as users and / or items can be constructed. For the relational network graph, usually, through the graph embedding algorithm, based on the initial characteristics of the nodes and the connection relationship between nodes, the embedding vector of each node in the relational network can be calculated, that is, the deeper characteristic information of the object can be obtained. The feature information of the object is represented by a vector of predetermined d...

AI Technical Summary

Problems solved by technology

However, the relationship network itself is dynamically changing. For example, in the social relationship graph, due to the continuous generation of new friend relationships and the dissolution of friend relationships, the structure of the relationship network at different moments is likely to be different.

Therefore, only using the network structure information at one moment to determine the feature information of each object does not make good use of the dynamic change information of the previous network structure.

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