Barbu, Catalin-Mihai; Ziegler, Jürgen:
Towards a Design Space for Personalizing the Presentation of Recommendations
In: Engineering Computer-Human Interaction in Recommender Systems : Proceedings of the Second Workshop on Engineering Computer-Human Interaction in Recommender Systems co-located with the 9th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2017) / Boratto, Ludovico; Carta, Salvatore; Fenu, Gianni (Hrsg.). - Second Workshop on Engineering Computer-Human Interaction in Recommender Systems (EnCHIReS 2017), 26. Juni 2017, Lisbon, Portugal - Aachen: RWTH Aachen, 2017 - (CEUR Workshop Proceedings ; 1945), S. 10 - 17
2017Buchaufsatz/Kapitel in TagungsbandOA Gold
InformatikFakultät für Ingenieurwissenschaften » Informatik und Angewandte Kognitionswissenschaft » Informatik » Interaktive Systeme / Interaktionsdesign
Titel in Englisch:
Towards a Design Space for Personalizing the Presentation of Recommendations
Autor*in:
Barbu, Catalin-MihaiUDE
LSF ID
58102
Sonstiges
der Hochschule zugeordnete*r Autor*in
;
Ziegler, JürgenUDE
GND
1015876811
LSF ID
3881
ORCID
0000-0001-9603-5272ORCID iD
Sonstiges
der Hochschule zugeordnete*r Autor*in
Open Access?:
OA Gold
Notiz:
OA platinum
Sprache des Textes:
Englisch
Schlagwort, Thema:
Recommender systems ; Personalization ; Design space ; Interactive control

Abstract in Englisch:

Although personalization plays a major role in the development of recommender systems, the presentation of recommendations{and especially the way in which it can be adapted to suit the user's needs{ has received relatively little attention from the research community. We introduce a design space for personalizing the presentation of recommendations and propose several dimensions that should be a part of it. Moreover, we present our initial insights about possible interactive mechanisms as well as potential evaluation criteria. Our goal is to provide a systematic way of designing personalized recommendation content, which should prove benecial for other researchers working on this topic. In the longer term, we are interested to investigate whether such personalized presentation implementations in uence the perceived trustworthiness of the recommendations.