Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adomavicius, G., Mobasher, B., Ricci, F., Tuzhilin, A.: Context-aware recommender systems. AI Magazine 32(3), 67–80 (2011)
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)
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)
Bangor, A., Kortum, P., Miller, J.: Determining what individual sus scores mean: Adding an adjective rating scale. Journal of Usability Studies 4(3) (2009)
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)
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)
Brooke, J.: Sus: A quick and dirty usability scale. Usability Evaluation in Industry 189, 194 (1996)
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)
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)
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)
Jannach, D., Zanker, M., Fuchs, M.: Constraint-based recommendation in tourism: A multiperspective case study. Information Technology & Tourism 11(2), 139–155 (2009)
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)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
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)
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)
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)
Sauro, J.: Measuring usability with the system usability scale (sus), http://www.measuringusability.com/sus.php (accessed: January 15, 2013)
Swarbrooke, J., Horner, S.: Consumer behaviour in tourism. Routledge (2007)
Tullis, T.S., Stetson, J.N.: A comparison of questionnaires for assessing website usability. In: Usability Professional Association Conference (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Braunhofer, M., Elahi, M., Ricci, F. (2014). Usability Assessment of a Context-Aware and Personality-Based Mobile Recommender System. In: Hepp, M., Hoffner, Y. (eds) E-Commerce and Web Technologies. EC-Web 2014. Lecture Notes in Business Information Processing, vol 188. Springer, Cham. https://doi.org/10.1007/978-3-319-10491-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-10491-1_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10490-4
Online ISBN: 978-3-319-10491-1
eBook Packages: Computer ScienceComputer Science (R0)