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Marketing Recommender Systems: A New Approach in Digital Economy

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  • Loredana MOCEAN
  • Ciprian Marcel POP

Abstract

Marketing information systems are those systems which make the gathering, processing, selection, storage, transmission and display of coordinated and continuous internal and external information. Includes systematic and formal methods used for managing all of an organization's information market. Recommendation systems are those systems that are widely used in online systems to suggest items that users might find interesting. These recommendations are generated using in particular two techniques: content-based and collaborative filtering. This paper aims to define a new system, namely Marketing Recommender System, a system that serves marketing and uses techniques and methods of the digital economy.

Suggested Citation

  • Loredana MOCEAN & Ciprian Marcel POP, 2012. "Marketing Recommender Systems: A New Approach in Digital Economy," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 16(4), pages 142-149.
    • Handle: RePEc:aes:infoec:v:16:y:2012:i:4:p:142-149
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    References listed on IDEAS

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    1. Zan Huang & Daniel D. Zeng & Hsinchun Chen, 2007. "Analyzing Consumer-Product Graphs: Empirical Findings and Applications in Recommender Systems," Management Science, INFORMS, vol. 53(7), pages 1146-1164, July.
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    Cited by:

    1. Behera, Rajat Kumar & Gunasekaran, Angappa & Gupta, Shivam & Kamboj, Shampy & Bala, Pradip Kumar, 2020. "Personalized digital marketing recommender engine," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).

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