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Ubiquitous recommender systems

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Abstract

Ubiquitous recommender systems combine characteristics from ubiquitous systems and recommender systems in order to provide personalized recommendations to users in ubiquitous environments. Although not a new research area, ubiquitous recommender systems research has not yet been reviewed and classified in terms of ubiquitous research and recommender systems research, in order to deeply comprehend its nature, characteristics, relevant issues and challenges. It is our belief that ubiquitous recommenders can nowadays take advantage of the progress mobile phone technology has made in identifying items around, as well as utilize the faster wireless connections and the endless capabilities of modern mobile devices in order to provide users with more personalized and context-aware recommendations on location to aid them with their task at hand. This work focuses on ubiquitous recommender systems, while a brief analysis of the two fundamental areas from which they emerged, ubiquitous computing and recommender systems research is also conducted. Related work is provided, followed by a classification schema and a discussion about the correlation of ubiquitous recommenders with classic ubiquitous systems and recommender systems: similarities inevitably exist, however their fundamental differences are crucial. The paper concludes by proposing UbiCARS: a new class of ubiquitous recommender systems that will combine characteristics from ubiquitous systems and context-aware recommender systems in order to utilize multidimensional context modeling techniques not previously met in ubiquitous recommender systems.

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Notes

  1. A proactive system acts considering and anticipating any problematic situations and events that could happen in the future.

  2. The author uses the term “resource” for the object to be annotated and “information” for the annotation itself. The present work uses the term “information resource” to refer to the annotation and no special term for the object to be annotated.

  3. We will refer to the user who is to be provided with recommendations as the “active user”.

  4. Things in the proximity could be other people, objects, or places.

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Authors and Affiliations

  1. Department of Computer Science, University of Cyprus, 1 University Avenue, P.O. Box 20537, 2109 , Nicosia, Cyprus

    Christos Mettouris & George A. Papadopoulos

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  1. Christos Mettouris
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  2. George A. Papadopoulos
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Correspondence to Christos Mettouris.

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Mettouris, C., Papadopoulos, G.A. Ubiquitous recommender systems. Computing 96, 223–257 (2014). https://doi.org/10.1007/s00607-013-0351-z

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