Loepp, Benedikt; Barbu, Catalin-Mihai; Ziegler, Jürgen:
Interactive Recommending : Framework, State of Research and Future Challenges
In: EnCHIReS 2016 : Engineering Computer-Human Interaction in Recommender Systems : Proceedings of the Workshop on Engineering Computer-Human Interaction in Recommender Systems co-located with the 8th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2016) - Workshop on Engineering Computer-Human Interaction in Recommender Systems, EnCHIReS 2016, 21. Juni 2016, Bruxelles, Belgium - Aachen: RWTH Aachen, 2016 - (CEUR workshop proceedings ; 1705), S. 3 - 13
2016Buchaufsatz/Kapitel in TagungsbandOA Gold
InformatikFakultät für Ingenieurwissenschaften » Informatik und Angewandte Kognitionswissenschaft » Informatik » Interaktive Systeme / Interaktionsdesign
Titel in Englisch:
Interactive Recommending : Framework, State of Research and Future Challenges
Autor*in:
Loepp, BenediktUDE
GND
1232038113
LSF ID
54109
ORCID
0000-0001-9059-5324ORCID iD
Sonstiges
der Hochschule zugeordnete*r 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 ; nteractive Recommending ; Models ; User Experience ; User Interfaces ; Survey

Abstract in Englisch:

In this paper, we present a framework describing the various aspects of recommender systems that can serve for empowering users by giving them more interactive control and transparency in the recommendation process. While conventional recommenders mostly operate like black boxes that cannot be influenced by the user, we identify four aspects properly connected with the recommendation algorithm—namely input data, user model, external con-text model and presentation—as essential points in which a system may be enhanced by additional interaction possibilities. In light of this framework, we take a closer look at prior and present solutions to integrate recommender systems with more interactivity and describe future research challenges. Regarding these challenges, we especially focus on experiences gained in our own work and outline future research we have planned in the area of interactive recommending.