Cross-network behavior association method for individual application

Abstract

The invention relates to a cross-network behavior association method for individual application. The method comprises the steps that firstly specific heterogeneous information of different networks is determined, topic modeling is carried out on the networks where the heterogeneous information is located respectively, and topic distribution expression of the corresponding information is obtained. Then, behavior information of users in the corresponding networks is aggregated to obtain the topic distribution expression of the users in the different networks respectively through calculation, and a set of network-associated user attribute factor vectors is learnt for the different networks through the one-to-one corresponding relation of topic distribution of the inter-network association users in the different networks. Finally, the topic distribution of the information of the different networks can be conversed mutually through the user attribute factor vectors of the different networks, and the aim of inter-network behavior information association is achieved. A topic model and user perception are introduced in, the association breaks through the limitation of semantic association, and perception is carried out under thinner granularity.

Description

technical field [0001] The invention belongs to the technical field of social media computing, and relates to a cross-network behavior association method oriented to individualized applications. Background technique [0002] With the development of Internet technology, various social media and network applications have emerged. In order to better socialize and obtain information, users use more and more social media services more and more frequently, which also makes users' information scattered on different network platforms, and it is impossible to analyze user behavior from a single network platform. understanding of users. Therefore, integrating the behaviors of users on different network platforms can better analyze user interests, thereby helping to design more accurate personalized applications. However, users' behaviors on different network platforms are often heterogeneous, and such heterogeneous cross-network behaviors often cannot be simply added one by one. Th...

AI Technical Summary

Problems solved by technology

However, none of these technologies have considered associating the heterogeneous behaviors of different networks from the perspective of associating users across networks, and then using the collective wisdom of users to discover the correlation patterns between different networks.

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