华东师范大学学报（自然科学版） >

2024 , Vol. 2024 >Issue 5: 45 - 56

DOI: https://doi.org/10.3969/j.issn.1000-5641.2024.05.005

学习评价与推荐

基于序列感知与多元行为数据的MOOCs知识概念推荐

  • 任俊霖 ,
  • 王欢 ,
  • 黄骁迪 ,
  • 李艳婷 ,
  • 琚生根
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  • 四川大学 计算机学院, 成都　610065

收稿日期: 2024-07-06

  录用日期: 2024-07-06

  网络出版日期: 2024-09-23

基金资助

国家自然科学基金 (62137001)

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Sequence-aware and multi-type behavioral data driven knowledge concept recommendation for massive open online courses

  • Junlin REN ,
  • Huan WANG ,
  • Xiaodi HUANG ,
  • Yanting LI ,
  • Shenggen JU
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  • College of Computer Science, Sichuan University, Chengdu　610065, China

Received date: 2024-07-06

  Accepted date: 2024-07-06

  Online published: 2024-09-23

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摘要

大规模在线开放课程 (massive open online courses, MOOCs) 中, 知识概念推荐旨在分析和提取平台上的学习记录, 进而为用户推荐个性化的知识概念, 避免主观盲目地挑选学习内容导致的低效性. 然而, 现有的知识概念推荐方法缺乏对用户行为数据的多维度利用, 例如序列信息和复杂类型交互. 鉴于此, 提出了一种基于序列感知与多元行为数据的MOOCs知识概念推荐方法, 提取知识概念的序列信息, 并与图卷积网络输出的特征通过注意力机制进行聚合, 参与用户下一个感兴趣知识概念的预测. 此外, 利用多元对比学习, 将用户兴趣偏好与不同的交互关系融合, 准确学习到复杂交互中的个性化特征. 在MOOCCube数据集上的实验结果表明, 所提出的方法在多项指标上优于现有的基线模型, 验证了其在知识概念推荐中的有效性和实用性.

关键词： 知识概念推荐; 序列建模; 对比学习

本文引用格式

任俊霖 , 王欢 , 黄骁迪 , 李艳婷 , 琚生根 . 基于序列感知与多元行为数据的MOOCs知识概念推荐[J]. 华东师范大学学报（自然科学版）, 2024 , 2024(5) : 45 -56 . DOI: 10.3969/j.issn.1000-5641.2024.05.005

Abstract

In massive open online courses (MOOCs), knowledge concept recommendation aims to analyze and extract learning records from a platform to recommend personalized knowledge concepts to users, thereby avoiding the inefficiencies caused by the blind selection of learning content. However, existing methods often lack comprehensive utilization of the multidimensional aspects of user behavior data, such as sequential information and complex interactions. To address this issue, we propose STRec, a sequence-aware and multi-type behavioral data driven knowledge concept recommendation method for MOOCs. STRec extracts the sequential information of knowledge concepts and combines it with the features produced by graph convolutional networks using an attention mechanism. This facilitates the prediction of a user's next knowledge concept of interest. Moreover, by employing multi-type contrastive learning, our method integrates user-interest preferences with various interaction relationships to accurately capture personalized features from complex interactions. The experimental results on the MOOCCube dataset demonstrate that the proposed method outperforms existing baseline models across multiple metrics, validating its effectiveness and practicality in knowledge concept recommendation.

Key words： knowledge concept recommendation; sequence modeling; contrastive learning

参考文献

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