research-article

Research on MOOC System Based on Bipartite Graph Context Collaborative Filtering Algorithm

Authors:

Bai JieAuthors Info & Claims

ICSCA '19: Proceedings of the 2019 8th International Conference on Software and Computer Applications

Pages 154 - 158

https://doi.org/10.1145/3316615.3316713

Published: 19 February 2019 Publication History

Abstract

The MOOC platform is a good tool to help people learn, but with the increase of resources and numbers on the platform, choosing a learning resource that suits you has become a big problem. Personalized recommendations can help learners alleviate the problem, and recommending algorithms is the core of the recommendation process. Based on the analysis of the existing algorithms in the existing MOOC platform, in order to improve the accuracy and effectiveness of the recommendation and solve the cold start problem, this paper proposes a bipartite graph context collaborative filtering algorithm based on the characteristics of the MOOC platform: first, combined with the context information, the collaborative user filtering algorithm is used to process the initial user-resource score data and obtain the nearest neighbor. second, the nearest neighbor was used to get new user-resource score data. Last, in order to get a lists of recommendations, the bipartite graph method was used to process the new data. The algorithm improves the recommendation quality of the algorithm by preprocessing the data set and constructing different contact quantized values. Finally, the effectiveness of the algorithm is verified by experiments.

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Cited By

El Mourabit IJai Andaloussi SOuchetto OMiyara M(2023)Exploring Collaborative Filtering Algorithms in MOOCs Recommender Systems: A Comprehensive OverviewProceedings of the 6th International Conference on Networking, Intelligent Systems & Security10.1145/3607720.3607742(1-5)Online publication date: 24-May-2023
https://dl.acm.org/doi/10.1145/3607720.3607742
Jung HJang YKim SKim H(2022)KPCR: Knowledge Graph Enhanced Personalized Course RecommendationAI 2021: Advances in Artificial Intelligence10.1007/978-3-030-97546-3_60(739-750)Online publication date: 19-Mar-2022
https://doi.org/10.1007/978-3-030-97546-3_60
Uddin IImran AMuhammad KFayyaz NSajjad M(2021)A Systematic Mapping Review on MOOC Recommender SystemsIEEE Access10.1109/ACCESS.2021.31010399(118379-118405)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3101039
Show More Cited By

Index Terms

Research on MOOC System Based on Bipartite Graph Context Collaborative Filtering Algorithm
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Collaborative filtering

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ICSCA '19: Proceedings of the 2019 8th International Conference on Software and Computer Applications

February 2019

611 pages

ISBN:9781450365734

DOI:10.1145/3316615

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of New Brunswick: University of New Brunswick

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Association for Computing Machinery

New York, NY, United States

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Published: 19 February 2019

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ICSCA '19

ICSCA '19: 2019 8th International Conference on Software and Computer Applications

February 19 - 21, 2019

Penang, Malaysia

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Cited By

El Mourabit IJai Andaloussi SOuchetto OMiyara M(2023)Exploring Collaborative Filtering Algorithms in MOOCs Recommender Systems: A Comprehensive OverviewProceedings of the 6th International Conference on Networking, Intelligent Systems & Security10.1145/3607720.3607742(1-5)Online publication date: 24-May-2023
https://dl.acm.org/doi/10.1145/3607720.3607742
Jung HJang YKim SKim H(2022)KPCR: Knowledge Graph Enhanced Personalized Course RecommendationAI 2021: Advances in Artificial Intelligence10.1007/978-3-030-97546-3_60(739-750)Online publication date: 19-Mar-2022
https://doi.org/10.1007/978-3-030-97546-3_60
Uddin IImran AMuhammad KFayyaz NSajjad M(2021)A Systematic Mapping Review on MOOC Recommender SystemsIEEE Access10.1109/ACCESS.2021.31010399(118379-118405)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3101039
Panyadee PWita R(2021)Integrating Graph-Based Document Recommendation in Digital Libraries of Theses CollectionRecent Advances in Information and Communication Technology 202110.1007/978-3-030-79757-7_22(223-232)Online publication date: 25-Jun-2021
https://doi.org/10.1007/978-3-030-79757-7_22

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