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mRHR: A Modified Reciprocal Hit Rank Metric for Ranking Evaluation of Multiple Preferences in Top-N Recommender Systems

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Artificial Intelligence: Methodology, Systems, and Applications (AIMSA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9883))

Abstract

Average reciprocal hit rank (ARHR) is a commonly used metric for ranking evaluation of top-n recommender systems. However, it suffers from an important shortcoming that it cannot be applied when the user has multiple preferences at a time. In order to overcome this problem, a modified version of ARHR metric is introduced and applied to grocery shopping domain by conducting a series of experiments on real-life data. The results show that the proposed measure is feasible for ranking evaluation of Top-N recommender systems in the cases where the users have multiple preferences at a time or a specific time interval.

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Acknowledgements

This work is partially supported by the Scientific and Technological Research Council of Turkey (TUBITAK).

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Authors and Affiliations

  1. Department of Information Systems, Middle East Technical University, 06800, Ankara, Turkey

    Serhat Peker & Altan Kocyigit

Authors
  1. Serhat Peker
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  2. Altan Kocyigit
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Correspondence to Serhat Peker .

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Editors and Affiliations

  1. Winston-Salem State University, Winston Salem, North Carolina, USA

    Christo Dichev

  2. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Gennady Agre

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Peker, S., Kocyigit, A. (2016). mRHR: A Modified Reciprocal Hit Rank Metric for Ranking Evaluation of Multiple Preferences in Top-N Recommender Systems. In: Dichev, C., Agre, G. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016. Lecture Notes in Computer Science(), vol 9883. Springer, Cham. https://doi.org/10.1007/978-3-319-44748-3_31

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  • DOI: https://doi.org/10.1007/978-3-319-44748-3_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44747-6

  • Online ISBN: 978-3-319-44748-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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