知识图与行为图混合嵌入的学习者偏好预测

李晓光; 宫磊; 李晓莉; 张昕; 于戈

doi:10.11959/j.issn.1000-436x.2021125

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知识图与行为图混合嵌入的学习者偏好预测

学术论文 | 更新时间：2024-06-05

- 知识图与行为图混合嵌入的学习者偏好预测
- Learner preferences prediction with mixture embedding of knowledge and behavior graph
- 通信学报 2021年42卷第8期页码：130-138
- 作者机构：
  
  1. 辽宁大学信息学院，辽宁沈阳 110036
  2. 东北大学计算机科学与工程学院，辽宁沈阳 110169
- 作者简介：
  
  [ "李晓光（1973- ），男，辽宁沈阳人，博士，辽宁大学教授、硕士生导师，主要研究方向为机器学习等" ]
  [ "宫磊（1996- ），男，辽宁大连人，辽宁大学硕士生，主要研究方向为推荐系统等" ]
  [ "李晓莉（1973- ），女，辽宁沈阳人，辽宁大学副教授，主要研究方向为大数据等" ]
  [ "张昕（1979- ），男，辽宁沈阳人，博士，辽宁大学副教授、硕士生导师，主要研究方向为复杂系统等" ]
  [ "于戈（1962- ），男，辽宁沈阳人，博士，东北大学教授，主要研究方向为数据库等" ]
- 基金信息：
  
  国家自然科学基金资助项目(U1811261);辽宁省教育厅高等学校基本科研基金资助项目(LWF201705)
- DOI：10.11959/j.issn.1000-436x.2021125
  中图分类号： TN92
- 网络首发：2021-08，
  
  纸质出版：2021-08-25
- 稿件说明：
移动端阅览
李晓光, 宫磊, 李晓莉, 等. 知识图与行为图混合嵌入的学习者偏好预测[J]. 通信学报, 2021,42(8):130-138.

Xiaoguang LI, Lei GONG, Xiaoli LI, et al. Learner preferences prediction with mixture embedding of knowledge and behavior graph[J]. Journal on Communications, 2021, 42(8): 130-138.
李晓光, 宫磊, 李晓莉, 等. 知识图与行为图混合嵌入的学习者偏好预测[J]. 通信学报, 2021,42(8):130-138. DOI： 10.11959/j.issn.1000-436x.2021125.

Xiaoguang LI, Lei GONG, Xiaoli LI, et al. Learner preferences prediction with mixture embedding of knowledge and behavior graph[J]. Journal on Communications, 2021, 42(8): 130-138. DOI： 10.11959/j.issn.1000-436x.2021125.

摘要

为了解决知识推荐模型中学习者偏好预测不准确、结构信息利用不充分等问题，针对学习者偏好预测模型中知识结构与学习者行为结构，提出了知识图与行为图混合嵌入的学习者偏好预测模型。首先，考虑使用图卷积的方式拟合结构信息，将图卷积扩展至知识图以及行为图，获取学习者整体性学习规律与个体性学习规律。然后，模型利用知识结构与行为结构的差异性拟合学习者个性化偏好，采用循环神经网络将学习者的过程性偏好进行编码与解码，获得学习者偏好分布。在真实数据集上的实验结果表明，所提模型对拟合学习者偏好效果良好。

Abstract

To solve the problems of inaccurate prediction of learner preference and insufficient utilization of structural information in the knowledge recommendation model

for the knowledge structure and learner behavior structure in the learner’s preference prediction model

the model of learner preferences predication with mixture embedding of knowledge and behavior graph was proposed.First

considering using graph convolution network (GCN) to fit structural information

GCN was extended to knowledge graph and behavior graph

the purpose of which was to obtain learners’ overall learning pattern and individual learning pattern.Then

the difference between knowledge structure and behavior structure was used to fit learners’ individual preferences

and recurrent neural network was used to encode and decode learners’ preferences to obtain the distribution of learners’ preference distribution.The experimental results on the real datasets demonstrate that the proposed model has a good effect on predicting learner preferences.

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references

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