Exploiting Internet of Things information to enhance venues’ recommendation accuracy

Exploiting Internet of Things information to enhance venues’ recommendation accuracy In this paper, we introduce a novel recommendation algorithm, which exploits data sourced from web services provided by the Internet of Things in order to produce more accurate venue recommendations. The proposed algorithm provides added value for the web services offered by the Internet of Things and enhances the state-of-the-art in this algorithm category by taking into account (a) web of things data regarding the contexts of the user and the context of the venues to be recommended (restaurants, movie theaters, etc.), such as the user’s geographical position, road traffic and weather conditions, (b) qualitative aspects of the venues, such as price, atmosphere or service, (c) the semantic similarity of venues and (d) the influencing factors between social network users, derived from user participation in social networks. The combination of these features leads to more accurate and better user-targeted recommendations. We also present a framework which incorporates the above characteristics, and we evaluate the presented algorithm, both in terms of performance and recommendation quality. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Service Oriented Computing and Applications Springer Journals

Exploiting Internet of Things information to enhance venues’ recommendation accuracy

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Publisher
Springer London
Copyright
Copyright © 2017 by Springer-Verlag London Ltd.
Subject
Computer Science; Computer Systems Organization and Communication Networks; Software Engineering/Programming and Operating Systems; e-Commerce/e-business; Computer Appl. in Administrative Data Processing; Management of Computing and Information Systems; IT in Business
ISSN
1863-2386
eISSN
1863-2394
D.O.I.
10.1007/s11761-017-0216-y
Publisher site
See Article on Publisher Site

Abstract

In this paper, we introduce a novel recommendation algorithm, which exploits data sourced from web services provided by the Internet of Things in order to produce more accurate venue recommendations. The proposed algorithm provides added value for the web services offered by the Internet of Things and enhances the state-of-the-art in this algorithm category by taking into account (a) web of things data regarding the contexts of the user and the context of the venues to be recommended (restaurants, movie theaters, etc.), such as the user’s geographical position, road traffic and weather conditions, (b) qualitative aspects of the venues, such as price, atmosphere or service, (c) the semantic similarity of venues and (d) the influencing factors between social network users, derived from user participation in social networks. The combination of these features leads to more accurate and better user-targeted recommendations. We also present a framework which incorporates the above characteristics, and we evaluate the presented algorithm, both in terms of performance and recommendation quality.

Journal

Service Oriented Computing and ApplicationsSpringer Journals

Published: Aug 14, 2017

References

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