Usability Assessment of a Context-Aware and Personality-Based Mobile Recommender System

  • Matthias Braunhofer
  • Mehdi Elahi
  • Francesco Ricci
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 188)


In this paper we present STS (South Tyrol Suggests), a context-aware mobile recommender system for places of interest (POIs) that integrates some innovative components, including: a personality questionnaire, i.e., a brief and entertaining questionnaire used by the system to learn users’ personality; an active learning module that acquires ratings-in-context for POIs that users are likely to have experienced; and a matrix factorization based recommendation module that leverages the personality information and several contextual factors in order to generate more relevant recommendations.

Adopting a system oriented perspective, we describe the assessment of the combination of the implemented components. We focus on usability aspects and report the end-user assessment of STS. It was obtained from a controlled live user study as well as from the log data produced by a larger sample of users that have freely downloaded and tried STS through Google Play Store. The result of the assessment showed that the overall usability of the system falls between “good” and “excellent”, it helped us to identify potential problems and it provided valuable indications for future system improvement.


Recommender systems context awareness mobile services active learning personality usability assessment 


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  1. 1.
    Adomavicius, G., Mobasher, B., Ricci, F., Tuzhilin, A.: Context-aware recommender systems. AI Magazine 32(3), 67–80 (2011)Google Scholar
  2. 2.
    Baltrunas, L., Kaminskas, M., Ludwig, B., Moling, O., Ricci, F., Aydin, A., Lüke, K.-H., Schwaiger, R.: InCarMusic: Context-aware music recommendations in a car. In: Huemer, C., Setzer, T. (eds.) EC-Web 2011. LNBIP, vol. 85, pp. 89–100. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Baltrunas, L., Ludwig, B., Peer, S., Ricci, F.: Context relevance assessment and exploitation in mobile recommender systems. Personal and Ubiquitous Computing 16(5), 507–526 (2012)CrossRefGoogle Scholar
  4. 4.
    Bangor, A., Kortum, P., Miller, J.: Determining what individual sus scores mean: Adding an adjective rating scale. Journal of Usability Studies 4(3) (2009)Google Scholar
  5. 5.
    Braunhofer, M., Elahi, M., Ge, M., Ricci, F.: Context dependent preference acquisition with personality-based active learning in mobile recommender systems. In: Zaphiris, P., Ioannou, A. (eds.) LCT 2014, Part II. LNCS, vol. 8524, pp. 105–116. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  6. 6.
    Braunhofer, M., Elahi, M., Ricci, F., Schievenin, T.: Context-aware points of interest suggestion with dynamic weather data management. In: 21st Conference on Information and Communication Technologies in Tourism, ENTER 2014 (2014)Google Scholar
  7. 7.
    Brooke, J.: Sus: A quick and dirty usability scale. Usability Evaluation in Industry 189, 194 (1996)Google Scholar
  8. 8.
    Codina, V., Ricci, F., Ceccaroni, L.: Exploiting the semantic similarity of contextual situations for pre-filtering recommendation. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds.) UMAP 2013. LNCS, vol. 7899, pp. 165–177. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  9. 9.
    Elahi, M., Braunhofer, M., Ricci, F., Tkalcic, M.: Personality-based active learning for collaborative filtering recommender systems. In: Baldoni, M., Baroglio, C., Boella, G., Micalizio, R. (eds.) AI*IA 2013. LNCS (LNAI), vol. 8249, pp. 360–371. Springer, Heidelberg (2013)Google Scholar
  10. 10.
    Gosling, S.D., Rentfrow, P.J., Swann Jr., W.B.: A very brief measure of the big-five personality domains. Journal of Research in Personality 37(6), 504–528 (2003)CrossRefGoogle Scholar
  11. 11.
    Jannach, D., Zanker, M., Fuchs, M.: Constraint-based recommendation in tourism: A multiperspective case study. Information Technology & Tourism 11(2), 139–155 (2009)CrossRefGoogle Scholar
  12. 12.
    Knijnenburg, B.P., Willemsen, M.C., Gantner, Z., Soncu, H., Newell, C.: Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction 22(4-5), 441–504 (2012)CrossRefGoogle Scholar
  13. 13.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRefGoogle Scholar
  14. 14.
    Panniello, U., Tuzhilin, A., Gorgoglione, M., Palmisano, C., Pedone, A.: Experimental comparison of pre-vs. post-filtering approaches in context-aware recommender systems. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 265–268. ACM (2009)Google Scholar
  15. 15.
    Park, M.-H., Park, H.-S., Cho, S.-B.: Restaurant recommendation for group of people in mobile environments using probabilistic multi-criteria decision making. In: Lee, S., Choo, H., Ha, S., Shin, I.C. (eds.) APCHI 2008. LNCS, vol. 5068, pp. 114–122. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Rentfrow, P.J., Gosling, S.D.: The do re mi’s of everyday life: the structure and personality correlates of music preferences. Journal of Personality and Social Psychology 84(6), 1236 (2003)CrossRefGoogle Scholar
  17. 17.
    Sauro, J.: Measuring usability with the system usability scale (sus), (accessed: January 15, 2013)
  18. 18.
    Swarbrooke, J., Horner, S.: Consumer behaviour in tourism. Routledge (2007)Google Scholar
  19. 19.
    Tullis, T.S., Stetson, J.N.: A comparison of questionnaires for assessing website usability. In: Usability Professional Association Conference (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Matthias Braunhofer
    • 1
  • Mehdi Elahi
    • 1
  • Francesco Ricci
    • 1
  1. 1.Free University of Bozen, BolzanoBolzanoItaly

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