Bipartite graph-based service recommendation method and apparatus

Abstract

Embodiments of the invention provide a bipartite graph-based service recommendation method and apparatus. The method comprises the steps of acquiring a service which each target user is interested in; constructing an initial user interest vector of each target user and an initial service topic vector of each service, wherein the user interest vector and the service topic vector are both probability vectors; generating a bipartite graph by taking the target users and the services as vertexes, and constructing a bipartite graph-based target function P according to the user interest vectors and the service topic vectors; solving the target function P to determine an element value of each user interest vector and an element value of each service topic vector; and performing service recommendation on the target users according to the user interest vectors and the service topic vectors. By applying the method and the apparatus, the calculation complexity of bipartite graph-based service recommendation is lowered, and the user interest vectors and the service topic vectors output by a model are all the probability vectors, so that more probability-based actual applications can be constructed conveniently.

Description

technical field [0001] The invention relates to the field of data mining and recommendation, in particular to a bipartite graph-based service recommendation method and device. Background technique [0002] The emergence and popularization of the Internet has led to a substantial increase in the information on the Internet, which has also brought great difficulty to users in obtaining really useful information, resulting in the phenomenon of information overload. In order to solve the problem of information overload, the existing technology proposes a service recommendation method, the basic processing idea of ​​which is: collect services that several users are interested in, and recommend services in the service set to the several users according to the services that the users are interested in, wherein, the The service collection is a collection of collected services. Wherein, the service that the user is interested in may be the service that the user has purchased, browse...

AI Technical Summary

Problems solved by technology

Although the MCMC sampling algorithm makes the LDA recommendation technology feasible, its mathematical nature is complex and the calculation complexity is high, which limits the application of the LDA-based recommendation technology.

The other two widely used recommendation technologies are collaborative filtering (Collaborative filtering technology) recommendation technology and latent factor model (Latent factor model) recommendation technology, which are relatively low in complexity, but the values ​​of the elements of the user and service vectors output by the model are arbitrary. Value, poor interpretability, difficult to combine with other models to build more complex practical applications

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