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

A prediction method and labeling technology, applied in prediction, calculation, computer parts and other directions, can solve the problem of not achieving good transfer improvement effect, and achieve the effect of avoiding negative transfer problems and improving training time and accuracy.

Active Publication Date: 2021-11-23
ZHEJIANG UNIV OF TECH
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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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  • A multi-source community label development trend prediction method based on a-tradaboost algorithm
  • A multi-source community label development trend prediction method based on a-tradaboost algorithm
  • A multi-source community label development trend prediction method based on a-tradaboost algorithm

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Embodiment Construction

[0026] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0027] refer to figure 1 and figure 2 , a multi-source community label development trend prediction method based on the A-TrAdaboost algorithm. The present invention uses the data in the Stack Exchange question-and-answer website for instance analysis, and the data adopts the creation time, post ID, and user ID of each post in the part of the question-and-answer community. , post tags and other information, build a tag network, extract the structural features and non-structural features corresponding to the tags, and carry out the construction and training of the proposed A-TrAdaboost model.

[0028] The present invention is specifically divided into following four steps:

[0029] Step 1: Build a Q&A community labeling network.

[0030] Step 2: Calculate the vector representation of the network structure in each comm...

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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...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q50/00G06K9/62
CPCG06Q10/04G06Q50/01G06F18/2148G06F18/24
Inventor 傅晨波郑永立赵明浩宣琦
Owner ZHEJIANG UNIV OF TECH
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