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Course Similarity Calculation Using Efficient Manifold Ranking

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Knowledge Science, Engineering and Management (KSEM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9403))

Abstract

Course Similarity Calculation aims at quantitatively computing the cross degree of the knowledge points two courses contain. However, the polysemy and synonym of various knowledge points lead to the main challenge for calculation effectiveness. Existing course similarity calculation methods are mainly based on the traditional text mining approaches such as Latent Semantic Indexing (LSI) and Term Frequency-Inverse Document Frequency (TFIDF). However, these methods calculate the similarity between two courses simply by their absolute pairwise distance, which significantly limits the effectiveness of capturing the semantic relevance among all the courses. In this paper, we propose a novel course similarity calculation method using Efficient Manifold Ranking (EMR), which improves the traditional methods by measuring course similarities considering the underlying intrinsic manifold structure on the whole dataset. Experimental results on a real world course database demonstrate the outstanding performance of our proposed method. Furthermore, we extend the proposed method to major similarity calculation.

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University, Jinan, China

    Bingjie Zhao & Xueqing Li

Authors
  1. Bingjie Zhao
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  2. Xueqing Li
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Corresponding author

Correspondence to Xueqing Li .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Songmao Zhang

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Martin Wirsing

  3. Southwest University, Chongqing, China

    Zili Zhang

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© 2015 Springer International Publishing Switzerland

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Zhao, B., Li, X. (2015). Course Similarity Calculation Using Efficient Manifold Ranking. In: Zhang, S., Wirsing, M., Zhang, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2015. Lecture Notes in Computer Science(), vol 9403. Springer, Cham. https://doi.org/10.1007/978-3-319-25159-2_38

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  • DOI: https://doi.org/10.1007/978-3-319-25159-2_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25158-5

  • Online ISBN: 978-3-319-25159-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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