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An Adjusted Recommendation List Size Approach for Users’ Multiple Item Preferences

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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

This paper describes the design and implementation of a novel approach to dynamically adjust the recommendation list size for multiple preferences of a user. By considering users’ earlier preferences, machine learning techniques are employed to estimate the optimal recommendation list size according to current conditions of users. The proposed approach has been evaluated on real-life data from grocery shopping domain by conducting a series of experiments. The results show that the proposed approach achieves better overall recommendation quality than the standard approach and it outperforms the benchmark method in efficiency by shortening the recommendation list while maintaining the effectiveness.

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Acknowledgments

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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Corresponding author

Correspondence to Serhat Peker .

Editor information

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). An Adjusted Recommendation List Size Approach for Users’ Multiple Item Preferences. 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_30

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  • DOI: https://doi.org/10.1007/978-3-319-44748-3_30

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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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