Skip to main content

Context-aware recommendations through context and activity recognition in a mobile environment


The mobile Internet introduces new opportunities to gain insight in the user’s environment, behavior, and activity. This contextual information can be used as an additional information source to improve traditional recommendation algorithms. This paper describes a framework to detect the current context and activity of the user by analyzing data retrieved from different sensors available on mobile devices. The framework can easily be extended to detect custom activities and is built in a generic way to ensure easy integration with other applications. On top of this framework, a recommender system is built to provide users a personalized content offer, consisting of relevant information such as points-of-interest, train schedules, and touristic info, based on the user’s current context. An evaluation of the recommender system and the underlying context recognition framework shows that power consumption and data traffic is still within an acceptable range. Users who tested the recommender system via the mobile application confirmed the usability and liked to use it. The recommendations are assessed as effective and help them to discover new places and interesting information.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4


  1. Adomavicius G, Sankaranarayanan R, Sen S, Tuzhilin A (2005) Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans Inf Syst 23(1):103–145

    Article  Google Scholar 

  2. Antoniou J, Pinto F, Simoes J, Pitsillides A (2010) Supporting context-aware multiparty sessions in heterogeneous mobile networks. Mobile Netw Appl 15:831–844

    Article  Google Scholar 

  3. Bao L, Intille S (2004) Activity recognition from user-annotated acceleration data. In: Ferscha A, Mattern F (eds) Pervasive computing. Lecture notes in computer science, vol 3001. Springer Berlin / Heidelberg, pp 1–17

    Chapter  Google Scholar 

  4. Biegel G, Cahill V (2004) A framework for developing mobile, context-aware applications. In: Proceedings of the second IEEE international conference on pervasive computing and communications (PerCom’04), PERCOM ’04. IEEE Computer Society, Washington, DC, USA, pp 361–365

    Chapter  Google Scholar 

  5. Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the fourteenth conference on uncertainty in artificial intelligence, UAI’98. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 43–52

    Google Scholar 

  6. Brown P, Bovey J, Chen X (1997) Context-aware applications: from the laboratory to the marketplace. IEEE Personal Communications 4(5):58–64

    Article  Google Scholar 

  7. CultuurNet-Vlaanderen (2012) Uitdatabank developer tools. Available at Accessed 15 Sept 2012

  8. Debaty P, Goddi P, Vorbau A (2005) Integrating the physical world with the web to enable context-enhanced mobile services. Mobile Networks and Applications 10(4):385–394

    Article  Google Scholar 

  9. Dey AK (2001) Understanding and using context. Personal and Ubiquitous Computing 5(1):4–7

    Article  Google Scholar 

  10. Dodson B (2012) Wikilocation. Available at Accessed 1 Oct 2012

  11. Foursquare (2012) Foursquare API. Available at Accessed 1 Oct 2012

  12. Gellersen H, Schmidt A, Beigl M (2002) Multi-sensor context-awareness in mobile devices and smart artifacts. Mobile Netw Appl 7:341–351

    Article  MATH  Google Scholar 

  13. Google (2012) Geocoding API. Available at Accessed 1 Oct 2012

  14. Google (2012) Places API. Available at Accessed 1 Oct 2012

  15. Han BJ, Rho S, Jun S, Hwang E (2010) Music emotion classification and context-based music recommendation. Multimedia Tools Appl 47(3):433–460

    Article  Google Scholar 

  16. Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53

    Article  Google Scholar 

  17. HLN (2012) Rss news feed. Available at Accessed 15 Nov 2012

  18. Iacono M, Krizek K, El-Geneidy A (2008) Access to destinations: how close is close enough? estimating accurate distance decay functions for multiple modes and different purposes. Tech. rep., University of Minnesota, Twin Cities. Minnesota Department of Transportation, Ref.: MN/RC 2008-11

  19. Inc S (2012) Humidity and Temperature Sensor for Mobile Devices. Available at

  20. Kenteris M, Gavalas D, Mpitziopoulos A (2010) A mobile tourism recommender system. In: Proceedings of the IEEE symposium on computers and communications, ISCC ’10. IEEE Computer Society, Washington, DC, USA, pp 840–845

    Chapter  Google Scholar 

  21. Kwapisz JR, Weiss GM, Moore SA (2011) Activity recognition using cell phone accelerometers. SIGKDD Explor Newsl 12(2):74–82

    Article  Google Scholar 

  22. Lee Mh, Kim J, Kim K, Lee I, Jee SH, Yoo SK (2009) Physical activity recognition using a single tri-axis accelerometer. In: Proceedings of the world congress on engineering and computer science, vol 1

  23. Lee SW, Mase K (2002) Activity and location recognition using wearable sensors. IEEE Pervasive Computing 1(3):24–32

    Article  Google Scholar 

  24. Oh JM, Moon N (2012) User-selectable interactive recommendation system in mobile environment. Multimed Tools Appl 57:295–313

    Article  Google Scholar 

  25. Oku K, Nakajima S, Miyazaki J, Uemura S (2006) Context-aware svm for context-dependent information recommendation. In: 7th international conference on mobile data management, 2006. MDM 2006. pp 109–109

  26. Ravi N, Dandekar N, Mysore P, Littman ML (2005) Activity recognition from accelerometer data. In: Proceedings of the 17th conference on Innovative applications of artificial intelligence, vol 3, IAAI’05. AAAI Press, pp 1541–1546

  27. Ricci F (2010) Mobile recommender systems. Information Technology & Tourism (ITT) 12(3), 205–231

    Article  Google Scholar 

  28. Schilit B, Theimer M (1994) Disseminating active map information to mobile hosts. IEEE Netw 8(5):22–32

    Article  Google Scholar 

  29. Schiller J, Voisard A (2004) Location-based services. Morgan Kaufmann

  30. Tiete Y, Schmitz S, Colpaert P iRail API (2012). Available at Accessed 8 Nov 2012

  31. Wagner J, Geleijnse G, van Halteren A (2011) Guidance and support for healthy food preparation in an augmented kitchen. In: Proceedings of the 2011 workshop on context-awareness in retrieval and recommendation, CaRR ’11. ACM, New York, NY, USA, pp 47–50

    Chapter  Google Scholar 

Download references


The authors would like to thank Bart Matté and Ewout Meyns for their programming work in this research project.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Toon De Pessemier.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

De Pessemier, T., Dooms, S. & Martens, L. Context-aware recommendations through context and activity recognition in a mobile environment. Multimed Tools Appl 72, 2925–2948 (2014).

Download citation

  • Published:

  • Issue Date:

  • DOI:


  • Context-aware
  • Recommender system
  • Activity recognition
  • Mobile