Recommendation method for technical labels in software question and answer community

Abstract

The invention provides a recommendation method oriented to labels in software question and answer community based on deep learning for the first time. The method comprises the steps of step 1, preprocessing; step 2, word meaning expression learning; step 3, phrase and sentence semantic learning; step 4, semantic fusion. The phase before preprocessing is a training stage, in which a dictionary is constructed and the structure and the weight of a model are stored. The phase after the pre-processing is a deployment stage, in which a new problem is converted into an index sequence, and the storedmodel is loaded to predict and recommend the most likely k labels, with k being a positive integer. Problem resource management and maintaining an existing label system in a software question and answer community can be provided. An automatic labeling service is provided for the users. The method aims to overcome the defects of existing label recommendation technology, and accuracy and efficiencyof the method can be improved, so that the label recommendation can be more practical in a software question and answer community.

Description

technical field [0001] The invention relates to a system recommendation method, in particular to a technology label recommendation method in a software question answering community. Background technique [0002] The software question-and-answer community refers to a question-and-answer community that focuses on solving technical problems and software development, such as the globally influential Stack Overflow community. Because of its huge number of users and resources (more than five million users, tens of millions of questions and answers), software resource management becomes complicated. In order to alleviate this problem, Stack Overflow requires users to give 1 to 5 Tags, that is, technical tags, when asking questions. However, due to the lack of sufficient understanding of the technical issues raised by some questioners and the great freedom in setting tags, this Tag system has become bloated and violated its original intention. Therefore, researchers have studied t...

AI Technical Summary

Problems solved by technology

[0005] Generally speaking, the existing technology considers the statistical information of the text, such as word frequency, etc., when recommending tags, ignoring the semantic information contained in the question itself, resulting in low recommendation accuracy and long time-consuming

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