基于问答语义匹配的知识社区新问题专家推荐方法

杜军威; 邹树林; 李浩杰; 江峰; 于旭; 胡强

doi:10.12263/DZXB.20220197

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基于问答语义匹配的知识社区新问题专家推荐方法

学术论文 | 更新时间：2025-12-08

- 基于问答语义匹配的知识社区新问题专家推荐方法
- Question Answering Semantic Matching-Based Expert Recommendation Method for New Questions in Knowledge Community
- 电子学报 2023年51卷第7期页码：1875-1888
- 作者机构：
  
  青岛科技大学信息科学技术学院，山东青岛266061
- 作者简介：
  
  [ "杜军威男，1974年7月生，山东威海人.博士、教授、博士生导师，主要研究方向为智能软件工程、推荐算法和自然语言处理.E-mail: djwqd@163.com" ]
  [ "邹树林男，1997年5月出生，山东泰安人.现为硕士研究生，主要研究方向为专家推荐系统.E-mail: shulin.zou@mails.qust.edu.cn" ]
  [ "江峰（通讯作者）男，1978年10月出生，江西彭泽人.博士、教授.主要研究方向为数据挖掘、机器学习.E-mail: jiangkong2002@163.com" ]
- 基金信息：
  
  国家自然科学基金(61973180;6217072142);山东省自然科学基金(ZR2021MF092;ZR2019MF014;ZR2019MF033;ZR2022MF326)
- DOI：10.12263/DZXB.20220197
  中图分类号： TP319
- 收稿：2022-02-22，
  
  修回：2022-10-07，
  
  纸质出版：2023-07-25
- 稿件说明：
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杜军威,邹树林,李浩杰等.基于问答语义匹配的知识社区新问题专家推荐方法[J].电子学报,2023,51(07):1875-1888.

DU Jun-wei,ZOU Shu-lin,LI Hao-jie,et al.Question Answering Semantic Matching-Based Expert Recommendation Method for New Questions in Knowledge Community[J].ACTA ELECTRONICA SINICA,2023,51(07):1875-1888.
杜军威,邹树林,李浩杰等.基于问答语义匹配的知识社区新问题专家推荐方法[J].电子学报,2023,51(07):1875-1888. DOI： 10.12263/DZXB.20220197.

DU Jun-wei,ZOU Shu-lin,LI Hao-jie,et al.Question Answering Semantic Matching-Based Expert Recommendation Method for New Questions in Knowledge Community[J].ACTA ELECTRONICA SINICA,2023,51(07):1875-1888. DOI： 10.12263/DZXB.20220197.

摘要

传统的知识社区专家推荐方法采用文本相似度匹配机理，并基于问题或专家描述来构建专家特征.这些方法没有利用问题与答案的语义匹配关系，因此难以充分挖掘专家回答问题的能力特征，影响推荐性能.提出一种基于综合历史和当前问答语义匹配的知识社区新问题的专家推荐方法（History-Now Semantics Expert RECommendation model，HNS-EREC）.首先，采用反馈评价和负采样技术来处理数据集中的两类不平衡现象；其次，基于问答语义来提取专家回答问题能力特征；最后，提出一种基于问答语义匹配的History-Now联合专家推荐模型，该模型能够实现面向专家的历史问答和当前问答的语义联合学习.实验结果表明，相对于其他方法，本文所提出的HNS-EREC方法在新问题专家推荐方面具有显著的优势.

Abstract

The traditional knowledge community expert recommendation methods adopt the mechanism of text similarity matching

and construct expert features based on questions or expert descriptions. These methods do not take advantage of the semantic matching relationship between questions and answers

hence it is difficult to fully exploit the features of experts' abilities to answer questions

which will affect the recommendation performance. In this paper

we propose a question answering semantic matching-based expert recommendation method

called History-Now Semantics Expert RECommendation model (HNS-EREC)

for new questions in knowledge community. First

the feedback evaluation and negative sampling techniques are used to handle two types of imbalances in data sets; second

the features of experts' abilities to answer questions are extracted based on question answering semantics; finally

a history-now joint expert recommendation model based on question answering semantic matching is proposed

which can realize the semantic joint learning of expert-oriented historical question answering and current question answering. Experimental results show that compared with other methods

the HNS-EREC method has obvious advantages in the expert recommendation for new questions.

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references

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