Usability Assessment of a Context-Aware and Personality-Based Mobile Recommender System
- 1.3k Downloads
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.
KeywordsRecommender systems context awareness mobile services active learning personality usability assessment
Unable to display preview. Download preview PDF.
- 1.Adomavicius, G., Mobasher, B., Ricci, F., Tuzhilin, A.: Context-aware recommender systems. AI Magazine 32(3), 67–80 (2011)Google Scholar
- 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
- 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.Brooke, J.: Sus: A quick and dirty usability scale. Usability Evaluation in Industry 189, 194 (1996)Google Scholar
- 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
- 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
- 17.Sauro, J.: Measuring usability with the system usability scale (sus), http://www.measuringusability.com/sus.php (accessed: January 15, 2013)
- 18.Swarbrooke, J., Horner, S.: Consumer behaviour in tourism. Routledge (2007)Google Scholar
- 19.Tullis, T.S., Stetson, J.N.: A comparison of questionnaires for assessing website usability. In: Usability Professional Association Conference (2004)Google Scholar