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

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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.

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Acknowledgement

This paper was supported by Grant Project Partnerships PCCA2013 “Intelligent management, monitoring and maintenance of pavements and roads using modern imaging techniques-PAV3 M” PN-II-PT-PCCA-2013-4-1762, no. 3/2014, Funder UEFISCDI, Executive Agency for Higher Education, Scientific Research, Development and Innovation Funding.

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  1. Department of Business Information Systems, Faculty of Economics and Business Administration, Babeş-Bolyai University, Cluj-Napoca, Romania

    Dan-Andrei Sitar-Tăut & Daniel Mican

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  1. Dan-Andrei Sitar-Tăut
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  2. Daniel Mican
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Correspondence to Daniel Mican.

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Sitar-Tăut, DA., Mican, D. MRS OZ: managerial recommender system for electronic commerce based on Onicescu method and Zipf’s law. Inf Technol Manag 21, 131–143 (2020). https://doi.org/10.1007/s10799-019-00309-w

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