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MRS OZ: managerial recommender system for electronic commerce based on Onicescu method and Zipf’s law

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  • Dan-Andrei Sitar-Tăut

    (Babeş-Bolyai University)

  • Daniel Mican

    (Babeş-Bolyai University)

Abstract

User decision intuition is challenging and complex, even if the user and product are known. Thus, recommending products is a management decision with high degree of incertitude. What if we are facing also the cold-start problem, like new products or visitors? This is a hot topic in recommender systems, tackled in variously, successfully or not. This perspective adds more incertitude to the existing uncertain scenario. Our philosophy is the shift from a user-centric view, hit by uncertainty, to a company-centric one taken in certainty circumstances, later to apply win–win approaches. We propose a multi-criteria algorithm -MRS OZ- for an ecommerce site RS that tackles the cold-start differently. It uses Onicescu method, being adapted according to Zipf’s Law, very popular in internet marketing. The paper opted for an exploratory research based on primary and secondary methods, consisting in literature review, 2-step survey addressed to 110 managers splat in 2 groups, and statistical analyses. The algorithm may substitute the human expertise on the given sample item list and criteria set. This work reveals that Onicescu method is suitable for recommender systems field, but relative inner category rankings and more domain related weight ratios strengthen the algorithm. Onicescu method has a wide applicability, but not for recommender systems. Also, the mixture with Zipf’s Law is completely experimental in research area.

Suggested Citation

  • Dan-Andrei Sitar-Tăut & Daniel Mican, 2020. "MRS OZ: managerial recommender system for electronic commerce based on Onicescu method and Zipf’s law," Information Technology and Management, Springer, vol. 21(2), pages 131-143, June.
    • Handle: RePEc:spr:infotm:v:21:y:2020:i:2:d:10.1007_s10799-019-00309-w
    DOI: 10.1007/s10799-019-00309-w
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    References listed on IDEAS

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    1. Rob J. Nadolski & Bert van den Berg & Adriana J. Berlanga & Hendrik Drachsler & Hans G.K. Hummel & Rob E.J.R. Koper & Peter B. Sloep, 2009. "Simulating Light-Weight Personalised Recommender Systems in Learning Networks: A Case for Pedagogy-Oriented and Rating-Based Hybrid Recommendation Strategies," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-4.
    2. Yoram Wind & Thomas L. Saaty, 1980. "Marketing Applications of the Analytic Hierarchy Process," Management Science, INFORMS, vol. 26(7), pages 641-658, July.
    3. Constantin Zopounidis & Michael Doumpos, 2017. "Multiple Criteria Decision Making," Post-Print hal-02880222, HAL.
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