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 

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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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