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International Conference on Web-Based Learning

ICWL 2013: Advances in Web-Based Learning – ICWL 2013 Workshops pp 32–41Cite as

A Novel Recommendation Relevancy Measure for Collaborative Filtering

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8390))

Abstract

Recommendation systems help people find their potential interests. In recommendation algorithms relevancy measures play an important role. Current relevancy measures often employ only user-item rating data or combine with contextual information to obtain related users or items. However, in some specific situations, these measures may not guarantee high accuracy or sufficient candidates. This paper solves these problems by proposing a novel recommendation relevancy measure, which indicates how worthy an item can be recommended to a user. In this paper, each interaction between a user and the recommendation system is regarded as a behavior represented with several features. The relevancy measure is achieved with a series of stepwise calculations and combinations on these features and behaviors. We evaluated the effectiveness of this measure against three other popular measures with a public dataset extracted from a commercial search engine. The experiment result shows that it can generate more recommendable items and achieves both high recall and precision.

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Notes

  1. 1.

    http://www.sogou.com/labs/dl/q-e.html. Corpus Search Engine Click-through Log (SogouQ). 2012-12-15.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (no. 61250010, 61272361, 61003263), the Program for Beijing Municipal Commission of Education (grant no.1320037010601), the 111 Project of Beijing Institute of Technology and the New Century Excellent Talents in University (grant no. NCET-06-0161).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Bo Shu, Zhendong Niu, Chunxia Zhang, Xiaotian Jiang & Hongping Fu

  2. Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China

    Wei Chen

Authors
  1. Bo Shu
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  2. Zhendong Niu
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  3. Chunxia Zhang
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  4. Xiaotian Jiang
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  5. Hongping Fu
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  6. Wei Chen
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Corresponding author

Correspondence to Zhendong Niu .

Editor information

Editors and Affiliations

  1. Dickson Computer Systems, Kowloon, Hong Kong SAR

    Dickson K. W. Chiu

  2. The University of Hong Kong, Pokfulam, Hong Kong SAR

    Minhong Wang

  3. University of Craiova, Craiova, Romania

    Elvira Popescu

  4. City University of Hong Kong, Hong Kong, Hong Kong SAR

    Qing Li

  5. City University of Hong Kong, Kowloon, Hong Kong SAR

    Rynson Lau

  6. Department of Computer Science and Information Engineering, National Central University, Jhongli City, Taiwan

    Timothy K. Shih

  7. Department of Electrical Engineering, National Cheng Kung University, Taiwan, Taiwan

    Chu-Sing Yang

  8. Digital Systems Centre for Research & Technology Hellas (CERTH), University of Piraeus Dept of Digital Systems, Piraeus, Greece

    Demetrios G. Sampson

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Shu, B., Niu, Z., Zhang, C., Jiang, X., Fu, H., Chen, W. (2015). A Novel Recommendation Relevancy Measure for Collaborative Filtering. In: Chiu, D., et al. Advances in Web-Based Learning – ICWL 2013 Workshops. ICWL 2013. Lecture Notes in Computer Science(), vol 8390. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46315-4_4

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  • DOI: https://doi.org/10.1007/978-3-662-46315-4_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-46314-7

  • Online ISBN: 978-3-662-46315-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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