Evaluating the impact of proactivity in the user experience of a context-aware restaurant recommender for Android smartphones

Journal of Systems Architecture

Volumn 59, Issue 9, 2013, Pages 748-758

  Gallego, Daniel ^a   Woerndl, Wolfgang ^b   Huecas, Gabriel ^c  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

^b TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^c UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

Author keywords

Context awareness; Evaluation; Mobile; Proactivity; Recommender system; User experience

Indexed keywords

CONTEXT-AWARENESS; EVALUATION; MOBILE; PRO ACTIVITIES; USER EXPERIENCE;

EMBEDDED SYSTEMS; RECOMMENDER SYSTEMS; ROBOTS; SMARTPHONES;

USER INTERFACES;

EID: 84885960632     PISSN: 13837621     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sysarc.2013.02.004     Document Type: Article

References (31)

1. W. Woerndl, J. Huebner, R. Bader, D. Gallego, A model for proactivity in mobile, context-aware recommender systems, in: Proceedings of the Fifth ACM Conference on Recommender Systems, 2011, pp. 273-276.
2. M. Baldauf, S. Dustdar, and F. Rosenberg A survey on context-aware systems Int. J. Ad Hoc Ubiquitous Comput. 2 4 2007 263 277 (Pubitemid 47433029)
3. G. Adomavicius, and A. Tuzhilin Context-aware recommender systems Recommender Systems Handbook 2011 Springer US 217 253
4. M. Kenteris, D. Gavalas, and D. Economou Electronic mobile guides: a survey Pers. Ubiquitous Comput. 15 1 2011 97 111
5. H. Stern, V. Pammer, S.N. Lindstaedt, A preliminary study on interruptibility detection based on location and calendar information, in: Proceedings of the Third Workshop on Context-Systems Design Evaluation and Optimisation, 2011.
6. L. Leiva, M. Böhmer, S. Gehring, A. Krüger, Back to the app: the costs of mobile application interruptions, in: Proceedings of the 14th International Conference on Human-Computer Interaction with Mobile Devices and Services, 2012, pp. 291-294.
7. J. Fogarty, S.E. Hudson, C.G. Atkeson, D. Avrahami, J. Forlizzi, S. Kiesler, J.C. Lee, and J. Yang Predicting human interruptibility with sensors ACM Trans. Comput.-Hum. Interact. 12 1 2005 119 146 (Pubitemid 40680056)
8. S. Rosenthal, A.K. Dey, and M.M. Veloso Using decision-theoretic experience sampling to build personalized mobile phone interruption models Pervasive vol. 6696 2011 Springer LNCS 170 187
9. N. Lathia, Using idle moments to record your health via mobile applications, in: Proceedings of the First ACM Workshop on Mobile Systems for Computational, Social Science, 2012, pp. 22-27.
10. N. Kern, B. Schiele, Context-aware notification for wearable computing, in: Proceedings of the 17th IEEE International Symposium on Wearable Computers, 2003, pp. 223-230.
11. J. Hong, E.-H. Suh, J. Kim, and S. Kim Context-aware system for proactive personalized service based on context history Expert Syst. Appl. 36 4 2009 7448 7457
12. H. Al Tair, M.J. Zemerly, M. Al-Qutayri, and M. Leida Architecture for context-aware pro-active recommender system Int. J. Multimedia Image Process. 2 1/2 2012 125 133
13. M. Modsching, R. Kramer, K. ten Hagen, U. Gretzel, Effectiveness of mobile recommender systems for tourist destinations: a user evaluation, in: Proceedings of the Fourth IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2007, pp. 663-668.
14. A. Ruiz-Iniesta, G. Jimenez-Diaz, M. Gomez-Albarran, Recommendation in repositories of learning objects: a proactive approach that exploits diversity and navigation-by-proposing, in: Proceedings of the Ninth IEEE International Conference on Advanced Learning Technologies, 2009, pp. 543-545.
15. O. Aritoni, and V. Negru A multi-agent recommendation system for energy efficiency improvement e-Technol. Netw. Dev.: Commun. Comput. Inf. Sci. 171 2011 156 170
16. F. Ricci Mobile recommender systems Int. J. Inf. Technol. Tourism 12 12 2011 205 231
17. A.K. Dey, G.D. Abowd, and D. Salber A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications Hum.-Comput. Interact. 16 2 2001 97 166 (Pubitemid 34216582)
18. Android, Developers: Service, 2011. Available from: < http://developer.android.com/reference/android/app/Service.html />.
19. M.D.E.-C. El Kettani, and B. En-Nasry MIdM: an open architecture for mobile identity management J. Converg. 2 2 2011 25 32
20. E.I. Sparling, S. Sen, Rating: how difficult is it?, in: Proceedings of the Fifth ACM Conference on Recommender Systems, 2011, pp. 149-156.
21. O. Averjanova, F. Ricci, Q. Nguyen, Map-based interaction with a conversational mobile recommender system, in: Proceedings of the Mobile Ubiquitous Computing, Systems, Services and Technologies, 2008, pp. 212-218.
22. J. Nielsen Quantitative Studies: How Many Users to Test? 2006 Jakob Nielsen's Alertbox June 26
23. L.A. Goodman Snowball sampling Ann. Math. Stat. 32 1 1961 148 170
24. R.Y. Shtykh, and Q. Jin A human-centric integrated approach to web information search and sharing Hum.-Centric Comput. Inf. Sci. 1 2 2011 1 37
25. R. Pan, G. Xu, B. Fu, P. Dolog, Z. Wang, and M. Leginus Improving recommendations by the clustering of tag neighbours J. Converg. 3 1 2011 13 20
26. J. Miller The user experience [internet] IEEE Internet Comput. 9 5 2005 90 92
27. Tek Yong Lim, Designing the next generation of mobile tourism application based on situation awareness, in: Proceedings of the Network of Ergonomics Societies Conference, 2012, pp. 1-7.
28. D. Gallego, E. Barra, S. Aguirre, G. Huecas, A model for generating proactive context-aware recommendations in e-learning systems, in: Proceedings of the 42nd ASEE/IEEE Frontiers in Education Conference, 2012, pp. 1364-1369.
29. D. Gallego, G. Huecas, J. Salvachúa, Generating context-aware recommendations using banking data in a mobile recommender system, in: Proceedings of the Sixth International Conference on Digital Society, 2012, pp. 73-78.
30. D. Gallego, G. Huecas, An empirical case of a context-aware mobile recommender system in a banking environment, in: Proceedings of the Third FTRA International Conference on Mobile, Ubiquitous, and Intelligent, Computing, 2012.
31. T. Teraoka Organization and exploration of heterogeneous personal data collected in daily life Hum.-Centric Comput. Inf. Sci. 2 1 2012 1 15