A multi-source community label development trend prediction method based on a-tradaboost algorithm

Abstract

A multi-source community label development trend prediction method based on the A-TrAdaboost algorithm, comprising the following steps: (1) constructing a label network in the question-and-answer community; (2) calculating the vector representation of the community structure in the source domain and the target domain (3) Calculate the structural similarity between the source domain and the target domain; (4) Construct a multi-source community tag popularity prediction model based on the A‑TrAdaboost algorithm. The present invention uses the network graph representation method to obtain the vector representation of each network, and then calculates the similarity between networks as the domain distance between different question-and-answer communities, and uses the similarity between network structures as a multi-source migration learning algorithm The initial weight of TrAdaboost can better avoid the negative migration problem in multi-source migration when predicting the popularity of new tags across communities, and improve the training time and accuracy of the model.

Description

technical field [0001] The invention relates to data mining and graph structure analysis technology, in particular to a multi-source community label development trend prediction method based on A-TrAdaboost algorithm. Background technique [0002] At present, with the widespread popularization of mobile Internet and smart devices, people's lifestyles have been changed. People are more inclined to express their opinions and collect the information they need on the Internet. Therefore, online question-and-answer communities have become more and more active and popular. Due to the huge number of posts in the Q&A community, the information obtained by users in the Q&A community is mainly screened and recommended based on the tags of the answers to the questions. As time goes by, the number of tags is also increasing. increasingly become the focus of attention. [0003] Fu Chenbo et al. (see literature [1] Fu C, Zheng Y, Li S, et al. Predicting the popularity of tags in StackExc...

AI Technical Summary

Problems solved by technology

In the problem of using transfer learning to predict the development trend of tag popularity in the Q&A community, measuring the similarity between different fields according to the difference between feature distributions cannot achieve a good transfer improvement effect

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