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Ignorant vs. Anonymous Recommendations

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Algorithms - ESA 2015

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9294))

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Abstract

We start with an unknown binary n ×m matrix, where the entries correspond to the preferences of n users on m items. The goal is to find at least one item per user that the user likes, with as few queries as possible. Since there are matrices where any algorithm performs badly without any preliminary knowledge of the input matrix, we reveal an anonymized version of the input matrix to the algorithm in the beginning of the execution. The input matrix is anonymized by shuffling the rows according to a randomly chosen hidden permutation. We observe that this anonymous recommendation problem can be seen as an adaptive variant of the Min Sum Set Cover problem and show that the greedy solution for the original version of the problem provides a constant approximation for the adaptive version.

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Authors and Affiliations

  1. ETH Zürich, Zürich, Switzerland

    Jara Uitto & Roger Wattenhofer

Authors
  1. Jara Uitto
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  2. Roger Wattenhofer
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Correspondence to Jara Uitto .

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Editors and Affiliations

  1. University of Technology, Eindhoven, The Netherlands

    Nikhil Bansal

  2. Sapienza University of Rome, Rome, Italy

    Irene Finocchi

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Uitto, J., Wattenhofer, R. (2015). Ignorant vs. Anonymous Recommendations. In: Bansal, N., Finocchi, I. (eds) Algorithms - ESA 2015. Lecture Notes in Computer Science(), vol 9294. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48350-3_83

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  • DOI: https://doi.org/10.1007/978-3-662-48350-3_83

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-48349-7

  • Online ISBN: 978-3-662-48350-3

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