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Soft Computing

, Volume 21, Issue 23, pp 7067–7075 | Cite as

Fitted Q-iteration and functional networks for ubiquitous recommender systems

  • Matteo Gaeta
  • Francesco Orciuoli
  • Luigi Rarità
  • Stefania Tomasiello
Methodologies and Application

Abstract

Ubiquitous recommender systems facilitate users on-location by personalized recommendations of items in the proximity via mobile devices. Due to a high variability of situations and preferences, an efficient resource processing is needed in order to assist the user in a proper way. In this paper, we consider a recommender system, able to learn preferences/habits of users through contextual information, such as location and time, using a new offline model-free approximate Q-iteration. Following the basic idea of Fitted Q-Iteration, the paper focuses on a computational scheme, based on functional networks, and that, unlike the well-known neural ones, does not require a large number of training samples. A preliminary case study, which deals with a shopping mall, is useful to show the approximation capabilities of the proposed approach.

Keywords

Q-learning Functional networks Ubiquitous context Recommender systems 

Notes

Compliance with ethical standards

Conflict of interest

All the authors declare that they have no conflict of interest.

Human and animals rights

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Dipartimento di Ingegneria dell’Informazione, Ingegneria Elettrica e Matematica ApplicataUniversity of SalernoFiscianoItaly
  2. 2.Dipartimento di Scienze Aziendali - Management and Innovation SystemsUniversity of SalernoFiscianoItaly
  3. 3.CO.RI.SA., COnsorzio RIcerca Sistemi ad AgentiUniversity of SalernoFiscianoItaly

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