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An Enhanced Distance Based Similarity Measure for User Based Recommendations

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Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016 (AISI 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 533))

Abstract

Internet users are overwhelmed with a large number of choices, consequently, there is a need to filter and prioritize relevant information. Recommender System (RS) solves this problem by searching through information provided by users similar to the active user. Precise determination of similar users is the keystone to accuracy of personalized recommendation and in this regard, the contribution of this paper is two-fold. First, an enhanced Distance based similarity measure is introduced. Second, a systematic evaluation is presented of the predictive performance of the proposed similarity measure against different similarity measures in recommendations based on user based Collaborative Filtering (CF). The evaluation encompasses both numeric and non-numeric measures against the proposed measure. The performance metrics are the recommendation accuracy (statistical and decision-making) and coverage. Experimental results on three real-world datasets show that the enhanced Distance based similarity outperforms all other similarity measures for user based recommendations in respect of the recommendation accuracy and coverage.

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Author information

Authors and Affiliations

  1. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt

    Yasmine M. Afify, Ibrahim F. Moawad, Nagwa L. Badr & Mohamed F. Tolba

Authors
  1. Yasmine M. Afify
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  2. Ibrahim F. Moawad
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  3. Nagwa L. Badr
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  4. Mohamed F. Tolba
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Corresponding author

Correspondence to Yasmine M. Afify .

Editor information

Editors and Affiliations

  1. Faculty of Computers & Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Dubai International Academic City, The British University, Dubai, United Arab Emirates

    Khaled Shaalan

  3. CS Dept. Faculty of Computers and Inform, Suez Canal University CS Dept. Faculty of Computers and Inform, Ismailia, Egypt

    Tarek Gaber

  4. Ahmed Orabi Square , Menouf, Egypt

    Ahmad Taher Azar

  5. Faculty of Computer & Information Scienc, Ain Shams University Faculty of Computer & Information Scienc, Cairo, Egypt

    M. F. Tolba

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Afify, Y.M., Moawad, I.F., Badr, N.L., Tolba, M.F. (2017). An Enhanced Distance Based Similarity Measure for User Based Recommendations. In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-319-48308-5_5

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  • DOI: https://doi.org/10.1007/978-3-319-48308-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48307-8

  • Online ISBN: 978-3-319-48308-5

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