Barbu, Catalin-Mihai; Ziegler, Jürgen:
User Model Dimensions for Personalizing the Presentation of Recommendations
In: Interfaces and Human Decision Making for Recommender Systems : Proceedings of the 4th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with ACM Conference on Recommender Systems (RecSys 2017) / Brusilovsky, Peter; de Gemmis, Marco; Felfernig, Alexander; Lops, Pasquale; O'Donovan, John; Tintarev, Nava; Willemsen, Martijn C. (Eds.). - IntRS 2017, 4th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, 27.08.2017, Como, Italy - Aachen: RWTH Aachen, 2017 - (CEUR Workshop Proceedings ; 1884), pp. 20 - 23
2017book article/chapter in ProceedingsOA Gold
Computer ScienceFaculty of Engineering » Computer Science and Applied Cognitive Science » Computer Science » Interactive Systems
Title in English:
User Model Dimensions for Personalizing the Presentation of Recommendations
Author:
Barbu, Catalin-MihaiUDE
LSF ID
58102
Other
connected with university
;
Ziegler, JürgenUDE
GND
1015876811
LSF ID
3881
ORCID
0000-0001-9603-5272ORCID iD
Other
connected with university
Open Access?:
OA Gold
Note:
OA platinum
Language of text:
English
Keyword, Topic:
Recommender systems ; Personalization ; User profile

Abstract in English:

Personalization in recommender systems has typically been applied to the underlying algorithms and to the predicted result sets. Meanwhile, the presentation of individual recommendations—specifically, the various ways in which it can be adapted to suit the user’s needs in a more effective manner—has received relatively little attention by comparison. A limiting factor for the design of such interactive and personalized presentations is the quality of the user data, such as elicited preferences, that is available to the recommender system. At the same time, many of the existing user models are not optimized sufficiently for this specific type of personalization. We present the results of an exploratory survey about users’ choices regarding the presentation of hotel recommendations. Based on our analysis, we propose several novel dimensions to the conventional user models exploited by recommender systems. We argue that augmenting user profiles with this range of information would facilitate the development of more interactive mechanisms for personalizing the presentation of recommendations. This, in turn, could lead to increased transparency and control over the recommendation process.