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Numerical Similarity Measures Versus Jaccard for Collaborative Filtering

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Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023 (AISI 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 184))

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Abstract

Collaborative filtering (CF) is an important method for recommendation systems, which are employed in many facets of our lives and are particularly prevalent in online-based commercial systems. The K-nearest neighbors (KNN) technique is a well-liked CF algorithm that uses similarity measurements to identify a user's closest neighbors in order to quantify the degree of dependency between the respective user and item pair. As a result, the CF approach is not only dependent on the choice of the similarity measure but also sensitive to it. However, some traditional “numerical” similarity measures, like cosine and Pearson, concentrate on the size of ratings, whereas Jaccard, one of the most frequently employed similarity measures for CF tasks, concerns the existence of ratings. Jaccard, in particular, is not a dominant measure, but it has long been demonstrated to be a key element in enhancing any measure. Therefore, this research focuses on presenting novel similarity measures by combining Jaccard with a multitude of numerical measures in our ongoing search for the most effective similarity measures for CF. Both existence and magnitude would benefit the combined measurements. Experimental results demonstrated that the combined measures are superior, surpassing all single measures across the considered assessment metrics.

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Acknowledgement

The authors would like to thank and appreciate the support they received from the Research Office of Zayed University for providing the necessary facilities to accomplish this work. This research has been supported by the Research Incentive Fund (RIF) Grant Activity Code: R22083—Zayed University, UAE.

Funding

This research has been supported by Research Incentive Fund (RIF) Grant Activity Code: R22083 – Zayed University, UAE.

Author information

Authors and Affiliations

  1. College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates

    Hassan I. Abdalla & Ali A. Amer

  2. Faculty of Computers and Informatics, Thamar University, Thamar, Yemen

    Yasmeen A. Amer & Basheer Mohamad Al-Maqaleh

  3. Loc Nguyen’s Academic Network, Long Xuyên, Vietnam

    Loc Nguyen

  4. Computer Science Department, Taiz University, Taiz, Yemen

    Ali A. Amer

Authors
  1. Hassan I. Abdalla
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  2. Yasmeen A. Amer
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  3. Loc Nguyen
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  4. Ali A. Amer
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  5. Basheer Mohamad Al-Maqaleh
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Contributions

All authors have been the key contributors in conception and design, implementing the approach and analyzing results of all experiments, and the preparation, writing and revising the manuscript.

Corresponding author

Correspondence to Ali A. Amer .

Editor information

Editors and Affiliations

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

    AboulElla Hassanien

  2. Port Said University, Port Fouad City, Egypt

    Rawya Y. Rizk

  3. Department of Operations Research and Statistics, The University of Belgrade, Faculty of Organizational Sciences, Belgrade, Serbia

    Dragan Pamucar

  4. Faculty of Science, Helwan University, Cairo, Egypt

    Ashraf Darwish

  5. Shulin District, Fujian University of Technology, New Taipei, Taiwan

    Kuo-Chi Chang

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The authors declare that they have no competing interests.

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Abdalla, H.I., Amer, Y.A., Nguyen, L., Amer, A.A., Al-Maqaleh, B.M. (2023). Numerical Similarity Measures Versus Jaccard for Collaborative Filtering. In: Hassanien, A., Rizk, R.Y., Pamucar, D., Darwish, A., Chang, KC. (eds) Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023. AISI 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 184. Springer, Cham. https://doi.org/10.1007/978-3-031-43247-7_20

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