Herrmanny, Katja; Löppenberg, Simone; Schwarz, Michael:
Investigating mechanisms for user integration in the activity goal recommendation process by interface design
In: Interfaces and Human Decision Making for Recommender Systems 2019 : Proceedings of the 6th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems / Brusilovsky, Peter; de Gemmis, Marco; Felfernig, Alexander; Lops, Pasquale; O’Donovan, John; Semeraro, Giovanni; Willemsen, Martijn C. (Hrsg.). - 6th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS2019), 19.09.2019, Copenhagen - Aachen: RWTH Aachen, 2019 - (CEUR Workshop Proceedings ; 2450), S. 46 - 54
2019Buchaufsatz/Kapitel in TagungsbandOA Gold
InformatikFakultät für Ingenieurwissenschaften » Informatik und Angewandte Kognitionswissenschaft » Informatik » Interaktive Systeme / Interaktionsdesign
Titel in Englisch:
Investigating mechanisms for user integration in the activity goal recommendation process by interface design
Autor*in:
Herrmanny, KatjaSCOPUSLSF; Löppenberg, SimoneSCOPUS; Schwarz, MichaelSCOPUSLSF
Open Access?:
OA Gold
Scopus ID:
Notiz:
OA platinum
Sprache des Textes:
Englisch
Schlagwort, Thema:
Activity tracking ; Goal setting ; Recommendation ; User empowerment ; User integration ; User interface

Abstract in Englisch:

In the field of physical activity recommendation, we have to deal with many confounding variables that lead to high result uncertainty. Assuming that users’ competence is an essential factor for reduction of the problem of inaccurate recommendations, we present and evaluate an approach on how to integrate users in the recommendation process. We investigate if and how interface element design can contribute to understanding, reflection and modification of the recommendation result. In the work described here, we use interface elements that allow for planning of physical activity goal striving. Results show that such interface elements can principally empower users, support recommendation reflection and stimulate user interaction with the recommendation.