Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View

SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval Virtual Event China July, 2020, pp. 79-88, 2020.

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students’ preferencematrix factorizationcontext informationknowledge concept recommendationmulti-layer perceptronMore(19+)
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We investigate the problem of the knowledge concept recommendation in massive open online courses system, which is often overlooked by massive open online courses recommendation system

Abstract:

Massive open online courses (MOOCs) are becoming a modish way for education, which provides a large-scale and open-access learning opportunity for students to grasp the knowledge. To attract students' interest, the recommendation system is applied by MOOCs providers to recommend courses to students. However, as a course usually consists o...More

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Introduction
  • Massive open online courses (MOOCs) are gradually becoming a mode of alternative education worldwide.
  • Coursera, edX, and Udacity, the three pioneering MOOC platforms, offer millions of user accesses to numerous courses from internationally renowned universities.
  • In China, millions of users study in XuetangX 1, which is one of the largest MOOC platforms[20], where thousands of courses are offered on various subjects.
  • The number of students in MOOCs is continuously growing, there are still some straits with MOOCs. A challenging problem for MOOCs is how to attract students to study continuously and efficiently on the platforms, where the overall course completion rate is lower than.
  • It requires better understanding and capturing of student interests
Highlights
  • In recent years, massive open online courses (MOOCs) are gradually becoming a mode of alternative education worldwide
  • We propose Attentional Heterogeneous Graph Convolutional Deep Knowledge Recommender (ACKRec ), an end-to-end framework for knowledge concept recommendation on massive open online courses platform
  • We investigate the problem of the knowledge concept recommendation in massive open online courses system, which is often overlooked by massive open online courses recommendation system
  • We propose Attentional Heterogeneous Graph Convolutional Deep Knowledge Recommender, an end-toend graph neural network based approach that naturally incorporates rich heterogeneous context side information into knowledge concept recommendation
  • To make use of rich context information in a more natural and intuitive way, we model the massive open online courses as a heterogeneous information network
  • We design an attention-based graph convolutional network to learn the representation of different entities via propagate context information under the guide of meta-path in an attentional way
Methods
  • : This can be viewed as a variant of ACKRec without the attention mechanism method, and concatenates different meta-paths together.
Results
  • Evaluation Metrics

    The authors evaluate all the methods in terms of the widely used metrics, including Hit Ratio of top-K items (HR@K) and Normalized Discounted Cumulative Gain of top-K items (NDCG@K) [12].
  • In this part of the experiments, the authors analyze how selection of meta-path combinations affect the performance of ACKRec , since a small number of high-quality meta-paths can lead to considerable performance [23]
  • The authors consider both single meta-path and their combinations.
  • To analyze the impact of different combinations in a small number of meta-paths, the authors use all three meta-paths to model the knowledge concept and study the performance with single user related meta-path and their combinations.
  • The authors find that using 30 latent factors can produce optimal performance
Conclusion
  • The authors investigate the problem of the knowledge concept recommendation in MOOCs system, which is often overlooked by MOOCs recommendation system.
  • The authors propose ACKRec , an end-toend graph neural network based approach that naturally incorporates rich heterogeneous context side information into knowledge concept recommendation.
  • To make use of rich context information in a more natural and intuitive way, the authors model the MOOCs as a heterogeneous information network.
  • The authors design an attention-based graph convolutional network to learn the representation of different entities via propagate context information under the guide of meta-path in an attentional way.
  • The promising experimental results illustrate the effectiveness of the proposed method
Summary
  • Introduction:

    Massive open online courses (MOOCs) are gradually becoming a mode of alternative education worldwide.
  • Coursera, edX, and Udacity, the three pioneering MOOC platforms, offer millions of user accesses to numerous courses from internationally renowned universities.
  • In China, millions of users study in XuetangX 1, which is one of the largest MOOC platforms[20], where thousands of courses are offered on various subjects.
  • The number of students in MOOCs is continuously growing, there are still some straits with MOOCs. A challenging problem for MOOCs is how to attract students to study continuously and efficiently on the platforms, where the overall course completion rate is lower than.
  • It requires better understanding and capturing of student interests
  • Methods:

    : This can be viewed as a variant of ACKRec without the attention mechanism method, and concatenates different meta-paths together.
  • Results:

    Evaluation Metrics

    The authors evaluate all the methods in terms of the widely used metrics, including Hit Ratio of top-K items (HR@K) and Normalized Discounted Cumulative Gain of top-K items (NDCG@K) [12].
  • In this part of the experiments, the authors analyze how selection of meta-path combinations affect the performance of ACKRec , since a small number of high-quality meta-paths can lead to considerable performance [23]
  • The authors consider both single meta-path and their combinations.
  • To analyze the impact of different combinations in a small number of meta-paths, the authors use all three meta-paths to model the knowledge concept and study the performance with single user related meta-path and their combinations.
  • The authors find that using 30 latent factors can produce optimal performance
  • Conclusion:

    The authors investigate the problem of the knowledge concept recommendation in MOOCs system, which is often overlooked by MOOCs recommendation system.
  • The authors propose ACKRec , an end-toend graph neural network based approach that naturally incorporates rich heterogeneous context side information into knowledge concept recommendation.
  • To make use of rich context information in a more natural and intuitive way, the authors model the MOOCs as a heterogeneous information network.
  • The authors design an attention-based graph convolutional network to learn the representation of different entities via propagate context information under the guide of meta-path in an attentional way.
  • The promising experimental results illustrate the effectiveness of the proposed method
Tables
  • Table1: Statistics of entities and relations for dataset
  • Table2: Notations and explanations
  • Table3: Different results from different combinations of meta-paths
Download tables as Excel
Related work
  • 5.1 Graph Neural Network in Heterogeneous Information Network

    Graphs play a crucial role in modern machine learning[5, 6]. Recently, graph neural networks[1, 4, 15, 26, 30, 31, 36] have become recurrent topics in machine learning, and both have broad applicability. However, in the real world, the graphs are usually heterogeneous. There are a few attempts heterogeneous information network setting. Wang et al [28] proposed DeepHGNN, an attentional heterogeneous graph neural network model to learn from the heterogeneous program behavior graph to guide the reidentification process. Wang et al [29] presented HAGNN, a Hierarchical Attentional Graph Neural Encoder and used it for program behavior graph analysis. Additionally, the GEM[17] model, a heterogeneous graph neural network approach for detecting malicious accounts at Alipay, has been presented. Unlike these approaches, our proposed model utilizes attentional graph convolutional networks for the representations of users and knowledge concepts in heterogeneous information networks.
Funding
  • This work is supported by NSF under grants III-1526499, III-1763325, III-1909323, CNS-1930941, by Science and Technology Project of the Headquarters of State Grid co., LTD under Grant No 5700- 202055267A-0-0-0, and by NKPs under grants 2018YFC0830804
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