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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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References

  1. Goldberg D et al (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61–70

    Article  Google Scholar 

  2. Manber U, Patel A, Robison J (2000) Experience with personalization on Yahoo! When designing Web personalization products, make sure you address all your users. Commun ACM 43(8):35–39

    Article  Google Scholar 

  3. Huang S-L (2011) Designing utility-based recommender systems for e-commerce: evaluation of preference-elicitation methods. Electron Commer Rec Appl 10(4):398–407

    Article  Google Scholar 

  4. Bouyahia T, et al (2017) Multi-criteria recommender approach for supporting intrusion response system. In: Cuppens F, et al (eds) Foundations and practice of security, Fps 2016. Springer International Publishing Ag, Cham, pp 51–67

  5. Yu S et al (2018) PAVE: personalized academic venue recommendation exploiting co-publication networks. J Netw Comput Appl 104:38–47

    Article  Google Scholar 

  6. Leskovec J, Rajaraman A, Ullman JD (2014) Mining of massive datasets, 2nd edn. Cambridge Univ Press, Cambridge, pp 1–467

    Book  Google Scholar 

  7. Lika B, Kolomvatsos K, Hadjiefthymiades S (2014) Facing the cold start problem in recommender systems. Expert Syst Appl 41(4):2065–2073

    Article  Google Scholar 

  8. Fremal S, Lecron F (2017) Weighting strategies for a recommender system using item clustering based on genres. Expert Syst Appl 77:105–113

    Article  Google Scholar 

  9. Rezaeimehr F et al (2018) TCARS: time- and community-aware recommendation system. Future Gener Comput Syst 78:419–429

    Article  Google Scholar 

  10. Ghoshal A, Kumar S, Mookerjee V (2015) Impact of recommender system on competition between personalizing and non-personalizing firms. J Manage Inf Syst 31(4):243–277

    Article  Google Scholar 

  11. Lin ZJ, Heng CS (2015) The paradoxes of word of mouth in electronic commerce. J Manage Inf Syst 32(4):246–284

    Article  Google Scholar 

  12. Solomon MR et al (2013) Consumer behaviour. A European perspective, 5th edn. Pearson

  13. Morawski J et al (2017) A fuzzy recommender system for public library catalogs. Int J Intell Syst 32(10):1062–1084

    Article  Google Scholar 

  14. Karpus A, et al (2016) Rating prediction with contextual conditional preferences. KDIR: Proceedings of the 8th international joint conference on knowledge discovery, knowledge engineering and knowledge management, vol 1, pp 419–424

  15. Nguyen PT et al (2015) Content-based recommendations via DBpedia and Freebase: a case study in the music domain. In Arenas M et al (eds) Semantic web—ISWC 2015, Pt I. Springer Int Publishing Ag, Cham, pp 605–621

  16. Marlinda L et al (2017) A multi-study program recommender system using ELECTRE multicriteria method. In: 5th international conference on cyber and IT service management (CITSM). IEEE, Denpasar, Bali

  17. Son J, Kim SB (2017) Content-based filtering for recommendation systems using multiattribute networks. Expert Syst Appl 89:404–412

    Article  Google Scholar 

  18. Shambour Q, Hourani M, Fraihat S (2016) An item-based multi-criteria collaborative filtering algorithm for personalized recommender systems. Int J Adv Comput Sci Appl 7(8):274–279

    Google Scholar 

  19. Nilashi M et al (2015) A multi-criteria collaborative filtering recommender system for the tourism domain using expectation maximization (EM) and PCA-ANFIS. Electron Commer Res Appl 14(6):542–562

    Article  Google Scholar 

  20. Nadolski RJ et al (2009) Simulating light-weight personalised recommender systems in learning networks: a case for pedagogy-oriented and rating-based hybrid recommendation strategies. J Artif Soc Soc Simul 12(1)

  21. Gurini DF et al (2018) Temporal people-to-people recommendation on social networks with sentiment-based matrix factorization. Future Gener Comput Syst 78:430–439

    Article  Google Scholar 

  22. Hwangbo H, Kim Y (2017) An empirical study on the effect of data sparsity and data overlap on cross domain collaborative filtering performance. Expert Syst Appl 89:254–265

    Article  Google Scholar 

  23. Margaris D, Vassilakis C, Georgiadis P (2018) Query personalization using social network information and collaborative filtering techniques. Future Gener Comput Syst 78:440–450

    Article  Google Scholar 

  24. Martin A, Zarate P, Camillieri G (2017) A multi-criteria recommender system based on users’ profile management. In: Zopounidis C, Doumpos M (eds) Multiple criteria decision making: applications in management and engineering. Springer International Publishing, Cham, pp 83–98

  25. Alexandridis G, Siolas G, Stafylopatis A (2017) Enhancing social collaborative filtering through the application of non-negative matrix factorization and exponential random graph models. Data Min Knowl Disc 31(4):1031–1059

    Article  Google Scholar 

  26. Eirinaki M et al (2018) Recommender systems for large-scale social networks: a review of challenges and solutions. Future Gener Comput Syst 78:413–418

    Article  Google Scholar 

  27. Gan MX, Jiang R (2018) FLOWER: fusing global and local associations towards personalized social recommendation. Future Gener Comput Syst 78:462–473

    Article  Google Scholar 

  28. Di Noia T et al (2017) Adaptive multi-attribute diversity for recommender systems. Inf Sci 382:234–253

    Article  Google Scholar 

  29. Guo JP et al (2018) Recommend products with consideration of multi-category inter-purchase time and price. Future Gener Comput Syst 78:451–461

    Article  Google Scholar 

  30. Vasto-Terrientes LD et al (2016) A hierarchical multi-criteria sorting approach for recommender systems. J Intell Inf Syst 46(2):313–346

    Article  Google Scholar 

  31. Brandtner P et al (2015) Multi-criteria selection in design science projects—a procedure for selecting foresight methods at the front end of innovation. In: Donnellan B et al (eds) New horizons in design science: broadening the research agend. Springer, Berlin, pp 295–310

    Google Scholar 

  32. Ilieş L, Bordean O, Crişan E (2006) Managementul firmei. Problemele decizionale şi planul de afaceri. Risoprint

  33. Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York

    Google Scholar 

  34. Dobre I, Bădescu AV (2002) Modelarea deciziilor economico-financiare. Conphys

  35. Roy B (1968) Classement et choix en présence de points de vue multiples (la méthode ELECTRE). RIRO 8:8

    Google Scholar 

  36. Amine Aït Younes RA (2000) Bernard Roy, ELECTRE IS—Manuel d’utilisation. Document du LAMSADE n° 118 bis. Vol. 1-2

  37. Roy B, Bertier P (1973) La méthode ELECTRE II—Une application au média-planning, O.’72, Editor. North-Holland Publishing Company, pp 291–302

  38. Figueira J, Moussea V, Roy B (ed) (2005) ELECTRE methods. Multiple criteria decision analysis: state of the art surveys. Springer, New York

  39. Roy B (1978) ELECTRE III: un algorithme de classements fondé sur une représentation floue des préférences en présence de criteres multiples. Cahiers du CERO 20(1):22

    Google Scholar 

  40. Roy B (1991) The outranking approach and the foundations of ELECTRE methods. Theor Decis 31(1):25

    Article  Google Scholar 

  41. Roy B (2001) Présentation et interprétation de la méthode ELECTRE TRI pour affecter des zones dans des catégories de risque. Université Paris-Dauphine

  42. Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1(1):83–98

    Google Scholar 

  43. Onicescu O (1970) Procedee de estimare comparativă a unor obiecte purtătoare de mai multe caracteristici. Revista de statistică

  44. Popescu V, Manea LR, Popescu G (2009) Hierarchical technological flowcharts specific to the textile chemical finishing according to the obtained quality indexes by using the Onicescu method. Management of Technological Changes, Vol 2, ed. C. Rusu, Komotini: Democritus Univ Thrace, pp 769–772

  45. Resteanu C, Popescu C, Popescu ME (2016) A scientometric method to evaluate the academic research performance. Stud Inf Control 25(4):433–444

    Google Scholar 

  46. Megyesi E et al (2014) Choosing the optimal type of external wall constructions for application in the field of passive houses. In: Geo conference on nano, bio and green—technologies for a sustainable future, Vol Ii. 2014, Stef92 Technology Ltd: Sofia, pp 65–72

  47. Broderick M (2015) What’s the price now? Commun ACM 58(4):21–23

    Article  Google Scholar 

  48. Baur AW et al (2014) Customer is King? A framework to shift from cost-to value-based pricing in software as a service: the case of business intelligence software. In: Li H, Mantymaki M, Zhang X (eds) Digital services and information intelligence. Springer, Berlin, pp 1–13

    Google Scholar 

  49. Anderson C (2006) the long tail: why the future of business is selling less of more. Hyperion

  50. Zipf GK (1950) Human behavior and the principle of least effort. J Clin Psychol 6(3):306

    Google Scholar 

  51. Chaffey D, Ellis-Chadwick F (2016) Digital marketing. Strategy, implementation and practice, 6th edn. Pearson

Download references

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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