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Modified Collaborative Filtering Algorithm Based on Multivariate Meta-similarity

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Knowledge Science, Engineering and Management (KSEM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8041))

Abstract

This paper further research the recommendation algorithm bases on the meta-similarity. We consider more information about users collect the items , and define the epidemic degree of the item(EDI) and user(EDU), modify the degree of overlapping of items, and analyze the effect of multivariate similarity in the recommendation system, then we present a modified collaborative filtering algorithm based on multivariate meta-similarity (MMSCF). The method reduces the influence of the EDI and EDU, limited the error to transfer, and enhances the similarity by multivariate meta-similarity. The experiments prove the new recommendation algorithm evaluated by the precision indexes of ranking score, precision and recall have achieved significantly improve.

This work is partially supported by the National Natural Science Foundation of China (Grant No. 71031002).

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Authors and Affiliations

  1. Institute of Systems Engineering, Dalian University of Technology, Dalian, 116024, China

    Pengyuan Xu & Yanzhong Dang

Authors
  1. Pengyuan Xu
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  2. Yanzhong Dang
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Editor information

Editors and Affiliations

  1. Faculty of Management and Economics, Dalian University of Technology, 116023, Dalian, China

    Mingzheng Wang

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Xu, P., Dang, Y. (2013). Modified Collaborative Filtering Algorithm Based on Multivariate Meta-similarity. In: Wang, M. (eds) Knowledge Science, Engineering and Management. KSEM 2013. Lecture Notes in Computer Science(), vol 8041. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39787-5_18

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  • DOI: https://doi.org/10.1007/978-3-642-39787-5_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39786-8

  • Online ISBN: 978-3-642-39787-5

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