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Recurrent neural network-based community question answering expert recommendation method

A technology of cyclic neural network and community question answering, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as reducing accuracy, achieve the effect of reducing dimensions, good experimental results, and simplifying complex processes

Active Publication Date: 2018-05-11
DALIAN UNIV OF TECH
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Problems solved by technology

This kind of method improves the recall rate of retrieval to a certain extent, but introduces a lot of noise information and reduces the accuracy.

Method used

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  • Recurrent neural network-based community question answering expert recommendation method
  • Recurrent neural network-based community question answering expert recommendation method
  • Recurrent neural network-based community question answering expert recommendation method

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Embodiment Construction

[0026] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the implementation of the present invention. example, not all examples. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0027] Such as figure 1 As shown, a community question answering expert recommendation method based on recurrent neural network, including the following steps:

[0028] Step 1. Obtain the data in the Stack Overflow data set corresponding to the question-and-answer community to be processed within a certain period of time and perform preprocessing; the data incl...

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Abstract

The invention discloses a recurrent neural network-based commodity question answering expert recommendation method. The method comprises the following steps of: preprocessing data; screening candidateexperts for all the users corresponding to a question answering community; constructing an expert user file corresponding to each candidate expert; carrying out training on the basis of word vectorsof specific domain knowledges so as to obtain a word vector search table; carrying out feature representation learning on the basis of a recurrent neural network model and constructing a community question answering expert recommendation model; and determining an expert recommendation sequence corresponding to a new question in a to-be-processed question answering community. According to the method, grammar and semantic information of sentences can be effectively expressed, and high-level features of sentence level can be mined, so that manual intervention is decreased and automatic training and learning can be carried out.

Description

technical field [0001] The invention relates to an expert recommendation method, in particular to a community question answering expert recommendation method based on a recurrent neural network. Background technique [0002] The rapid development of the Internet has changed the way people communicate, and more and more people increasingly rely on their Internet communities to obtain information and consult professional knowledge, such as public discussion forums, community-building encyclopedias (such as Wikipedia) and community Q&A ( CQA) website. Among all these Internet services, using CQA to find the information you need and share your expertise has recently gained wide popularity among the public. As more and more questions are answered on the community question and answer website, the community question and answer has established a large-scale knowledge warehouse that can be freely obtained. While meeting the needs of the questioner, it can also provide valuable info...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06F17/27
CPCG06F16/3322G06F40/253G06F40/284G06F40/30
Inventor 王健孙吉庆林鸿飞
Owner DALIAN UNIV OF TECH
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