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Context and Activity Recognition for Personalized Mobile Recommendations

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Web Information Systems and Technologies (WEBIST 2013)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 189))

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Abstract

Through the use of mobile devices, contextual information about users can be derived to use as an additional information source for traditional recommendation algorithms. This paper presents a framework for detecting the context and activity of users by analyzing sensor data of a mobile device. The recognized activity and context serves as input for a recommender system, which is built on top of the framework. Through context-aware recommendations, users receive a personalized content offer, consisting of relevant information such as points-of-interest, train schedules, and touristic info. An evaluation of the recommender system and the underlying context-recognition framework demonstrates the impact of the response times of external information providers. The data traffic on the mobile device required for the recommendations shows to be limited. A user evaluation confirms the usability and attractiveness of the recommender. The recommendations are experienced as effective and useful for discovering new venues and relevant information.

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Author information

Authors and Affiliations

  1. Dept. of Information Technology, iMinds - WiCa - Ghent University, Gaston Crommenlaan 8, Box 201, 9050, Ghent, Belgium

    Toon De Pessemier, Simon Dooms, Kris Vanhecke, Bart Matté, Ewout Meyns & Luc Martens

Authors
  1. Toon De Pessemier
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  2. Simon Dooms
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  3. Kris Vanhecke
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  4. Bart Matté
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  5. Ewout Meyns
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  6. Luc Martens
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Corresponding author

Correspondence to Toon De Pessemier .

Editor information

Editors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Karl-Heinz Krempels

  2. Virtual Vehicle Research Center, Graz, Austria

    Alexander Stocker

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De Pessemier, T., Dooms, S., Vanhecke, K., Matté, B., Meyns, E., Martens, L. (2014). Context and Activity Recognition for Personalized Mobile Recommendations. In: Krempels, KH., Stocker, A. (eds) Web Information Systems and Technologies. WEBIST 2013. Lecture Notes in Business Information Processing, vol 189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44300-2_15

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  • DOI: https://doi.org/10.1007/978-3-662-44300-2_15

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44299-9

  • Online ISBN: 978-3-662-44300-2

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