Abstract
The use of Recommender Systems (RSs) to support customers and sellers in Business-to-Consumer activities is emerged in the last years and several RSs have been proposed on different e-Commerce platforms to provide customers with automatic and personalized suggestions. However, the information such tools catch in supporting B2C customers in their Web activities then are unused to support them on the traditional commerce. In other words, these two environments operate separately without implementing synergistic actions to share knowledge and experiences between these two modality of commerce. In this paper, we propose a distributed RS, called ICR-IoT, based on a multi-tiered agent architecture, conceived to realize such a synergy. The key of our idea is that of using a tier, based on the Internet-of-Things technology, designed to catch information about customers of traditional markets in order to generate very effective suggestions to support commercial activities both on a traditional store as well as on an e-Commerce site.
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This work has been developed within by the Networks and Complex Systems (NeCS) Laboratory, Dep. DICEAM, University Mediterranea of Reggio Calabria.
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Fortino, G., Guerrieri, A., Rosaci, D., Sarné, G.M.L. (2018). Integrating Traditional Stores and e-Commerce into a Multi-tiered Recommender System Architecture Supported by IoT. In: Fortino, G., Ali, A., Pathan, M., Guerrieri, A., Di Fatta, G. (eds) Internet and Distributed Computing Systems. IDCS 2017. Lecture Notes in Computer Science(), vol 10794. Springer, Cham. https://doi.org/10.1007/978-3-319-97795-9_5
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