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Exploiting User Movements to Derive Recommendations in Large Facilities

  • Conference paper
Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference (DCAI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 801))

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Abstract

This paper provides an innovative approach for taking advantage of user’s movement data as implicit user feedback for deriving recommendations in large facilities. By means of a real-world museum scenario a beacon infrastructure for tracking sojourn times is presented. Then we show how sojourn times can be integrated in a collaborative filtering algorithm approach in order to outcome accurate recommendations.

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Notes

  1. 1.

    https://www.landesmuseum-hannover.niedersachsen.de/.

  2. 2.

    https://developer.apple.com/ibeacon/.

  3. 3.

    https://developers.google.com/beacons/.

References

  1. Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 217–253. Springer (2011)

    Google Scholar 

  2. Baltrunas, L., Amatriain, X.: Towards time-dependant recommendation based on implicit feedback. In: Workshop on Context-Aware Recommender Systems (CARS 2009) (2009)

    Google Scholar 

  3. Benouaret, I., Lenne, D.: Personalizing the museum experience through context-aware recommendations. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 743–748 (2015)

    Google Scholar 

  4. Billhardt, H., Dunkel, J., Fernandez, A., Lujak, M., Hermoso, R., Ossowski, S.: A proposal for situation-aware evacuation guidance based on semantic technologies. In: 4th International Conference on Agreement Technologies (AT2016), Valencia, vol. 2016, pp. 493–508 (2016)

    Google Scholar 

  5. Chianese, A., Marulli, F., Moscato, V., Piccialli, F.: SmARTweet: a location-based smart application for exhibits and museums. In: International Conference on Signal-Image Technology and Internet-Based Systems (SITIS), pp. 408–415. IEEE Computer Society (2013)

    Google Scholar 

  6. Ghiani, G., Paternò, F., Santoro, C., Spano, L.D.: Ubicicero: a location-aware, multi-device museum guide. Interact. Comput. 21(4), 288–303 (2009)

    Article  Google Scholar 

  7. Hermoso, R., Dunkel, J., Krause, J.: Situation awareness for push-based recommendations in mobile devices. In: 19th International Conference on Business Information Systems (BIS), Leibzig, vol. 2016, pp. 117–129 (2016)

    Google Scholar 

  8. Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 263–272, December 2008

    Google Scholar 

  9. Lee, T.Q., Park, Y., Park, Y.-T.: A time-based approach to effective recommender systems using implicit feedback. Expert Syst. Appl. 34(4), 3055–3062 (2008)

    Article  Google Scholar 

  10. Lujak, M., Billhardt, H., Dunkel, J., Fernández, A., Hermoso, R., Ossowski, S.: A distributed architecture for real-time evacuation guidance in large smart buildings. Comput. Sci. Inf. Syst. 14(1), 257–282 (2017)

    Article  Google Scholar 

  11. Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D.: Context aware computing for the internet of things: a survey. IEEE Commun. Surv. Tutorials 16(1), 414–454 (2014)

    Article  Google Scholar 

  12. Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)

    Article  Google Scholar 

  13. Rubino, I., Xhembulla, J., Martina, A., Bottino, A., Malnati, G.: MusA: using indoor positioning and navigation to enhance cultural experiences in a museum. Sensors 13(12), 17445–17471 (2013)

    Article  Google Scholar 

  14. Yürür, O., Liu, C.H., Sheng, Z., Leung, V.C.M., Moreno, W., Leung, K.K.: Context-awareness for mobile sensing: a survey and future directions. IEEE Commun. Surv. Tutorials 18(1), 68–93 (2016)

    Article  Google Scholar 

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Acknowledgments

This work has been supported by the projects DGA-FSE, TIN2015-65515-C4-4-R, TIN2016-78011-C4-3-R and Universidad de Zaragoza - Ibercaja-CAI fellowship IT 9/17.

Author information

Authors and Affiliations

  1. Hannover University of Applied Sciences and Arts, Hannover, Germany

    Jürgen Dunkel & Florian Rückauf

  2. University of Zaragoza, Zaragoza, Spain

    Ramón Hermoso

Authors
  1. Jürgen Dunkel
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  2. Ramón Hermoso
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  3. Florian Rückauf
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Corresponding author

Correspondence to Jürgen Dunkel .

Editor information

Editors and Affiliations

  1. BISITE Digital Innovation Hub, University of Salamanca, Salamanca, Spain

    Sara Rodríguez

  2. BISITE Digital Innovation Hub, University of Salamanca, Salamanca, Spain

    Javier Prieto

  3. GECAD - Instituto Superior de Engenharia, Porto, Portugal

    Pedro Faria

  4. Department of Computer Science and Production Management, University of Zielona Góra, Zielona Góra, Poland

    Sławomir Kłos

  5. Computing Science and Artificial Intelligence, Rey Juan Carlos University, Móstoles, Madrid, Spain

    Alberto Fernández

  6. Basque Center for Applied Mathematics, Bilbao, Spain

    Santiago Mazuelas

  7. Basque Center for Applied Mathematics, Universidad de Alcalá, Alcalá de Henares, Spain

    M. Dolores Jiménez-López

  8. Departamento de Informática y Automática, University of Salamanca, Salamanca, Spain

    María N. Moreno

  9. Departamento de Sistemas Informáticos, University of Castilla-La Mancha, Albacete, Spain

    Elena M. Navarro

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Dunkel, J., Hermoso, R., Rückauf, F. (2019). Exploiting User Movements to Derive Recommendations in Large Facilities. In: Rodríguez, S., et al. Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-319-99608-0_14

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