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Recommending Products and Services Belonging to Online Businesses Using Intelligent Agents

Author

Listed:
  • Adrian Alexandrescu

    (Faculty of Automatic Control and Computer Engineering, Gheorghe Asachi Technical University of Iasi, Iasi, 700050 Romania)

  • Cristian Nicolae Butincu

    (Faculty of Automatic Control and Computer Engineering, Gheorghe Asachi Technical University of Iasi, Iasi, 700050 Romania)

  • Mitică Craus

    (Faculty of Automatic Control and Computer Engineering, Gheorghe Asachi Technical University of Iasi, Iasi, 700050 Romania)

Abstract

A sure method for a business organization to sell more products is to expand its customer base and to have its products recommended by other organizations and individuals. This paper takes a look at the techniques used by shopping websites in order to entice the user in purchasing their products, and proposes a system for recommending products and services provided by different online businesses to potential customers. The solution is built upon a service-oriented architecture that allows businesses to share information regarding customers’ purchases while taking into account the user privacy issue. Intelligent agents, which rely on a product type association dynamically weighted graph, are employed in order to obtain and to process the information needed to make the suggestions. The use of intelligent agents significantly improves the quality of the recommendations made by the system. This improvement is achieved by suggesting products and services depending on other users’ purchasing patterns while also considering the different product types and quantities sold by the business organizations that are part of the system.

Suggested Citation

  • Adrian Alexandrescu & Cristian Nicolae Butincu & Mitică Craus, 2017. "Recommending Products and Services Belonging to Online Businesses Using Intelligent Agents," Service Science, INFORMS, vol. 9(4), pages 338-348, December.
    • Handle: RePEc:inm:orserv:v:9:y:2017:i:4:p:338-348
    DOI: 10.1287/serv.2017.0188
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    References listed on IDEAS

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    1. Radha Mookerjee & Subodha Kumar & Vijay S. Mookerjee, 2012. "To Show or Not Show: Using User Profiling to Manage Internet Advertisement Campaigns at Chitika," Interfaces, INFORMS, vol. 42(5), pages 449-464, October.
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    Cited by:

    1. Yang, Guangyong & Ji, Guojun, 2022. "The impact of cross-selling on managing consumer returns in omnichannel operations," Omega, Elsevier, vol. 111(C).
    2. Guangyong Yang & Guojun Ji & Kim Hua Tan, 2022. "Impact of artificial intelligence adoption on online returns policies," Annals of Operations Research, Springer, vol. 308(1), pages 703-726, January.
    3. Liao, Xia & Zheng, Yu-Hao & Shi, Guicheng & Bu, Huimei, 2024. "Automated social presence in artificial-intelligence services: Conceptualization, scale development, and validation," Technological Forecasting and Social Change, Elsevier, vol. 203(C).

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