Abstract
One of the most important steps in building a recommender system is the interaction design process, which defines how the recommender system interacts with a user. It also shapes the experience the user gets, from the point she registers and provides her preferences to the system, to the point she receives recommendations generated by the system. A proper interaction design may improve user experience and hence may result in higher usability of the system, as well as, in higher satisfaction.
In this paper, we focus on the interaction design of a mobile food recommender system that, through a novel interaction process, elicits users’ long-term and short-term preferences for recipes. User’s long-term preferences are captured
by asking the user to rate and tag familiar recipes, while for collecting the short-term preferences, the user is asked to select the ingredients she would like to include in the recipe to be prepared. Based on the combined exploitation of both
types of preferences, a set of personalized recommendations is generated. We conducted a user study measuring the usability of the proposed interaction. The results of the study show that the majority of users rates the quality of the recommendations high and the system achieves usability scores above the standard benchmark.
In this paper, we focus on the interaction design of a mobile food recommender system that, through a novel interaction process, elicits users’ long-term and short-term preferences for recipes. User’s long-term preferences are captured
by asking the user to rate and tag familiar recipes, while for collecting the short-term preferences, the user is asked to select the ingredients she would like to include in the recipe to be prepared. Based on the combined exploitation of both
types of preferences, a set of personalized recommendations is generated. We conducted a user study measuring the usability of the proposed interaction. The results of the study show that the majority of users rates the quality of the recommendations high and the system achieves usability scores above the standard benchmark.
Original language | English |
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Title of host publication | Interfaces and Human Decision Making for Recommender Systems |
Subtitle of host publication | Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with ACM Conference on Recommender Systems (RecSys 2015) |
Editors | John O'Donovan, Alexander Felfernig, Nava Tintarev, Peter Brusilovsky, Giovanni Semeraro, Pasquale Lops |
Publisher | CEUR Workshop Proceedings |
Pages | 49-52 |
Number of pages | 4 |
Volume | 1438 |
Publication status | Published - 2015 |
Externally published | Yes |
Event | 9th ACM Conference on Recommender Systems, RecSys 2015 - Vienna, Austria Duration: 16 Sept 2015 → 20 Sept 2015 |
Publication series
Name | CEUR Workshop Proceedings |
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Publisher | RWTH Aachen University |
ISSN (Print) | 1613-0073 |
Conference
Conference | 9th ACM Conference on Recommender Systems, RecSys 2015 |
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Country/Territory | Austria |
City | Vienna |
Period | 16/09/15 → 20/09/15 |