Loepp, Benedikt:
Recommender Systems Alone Are Not Everything : Towards a Broader Perspective in the Evaluation of Recommender Systems
In: Proceedings of the Perspectives on the Evaluation of Recommender Systems Workshop 2022 : co-located with the 16th ACM Conference on Recommender Systems (RecSys 2022) - PERSPECTIVES 2022, 22. September 2022, Seattle, USA - Aachen: RWTH Aachen, 2022 - (CEUR workshop proceedings ; 3228)
2022book article/chapter in ProceedingsOA Diamond
Computer ScienceFaculty of Engineering » Computer Science and Applied Cognitive Science » Computer Science » Interactive Systems
Title in English:
Recommender Systems Alone Are Not Everything : Towards a Broader Perspective in the Evaluation of Recommender Systems
Author:
Loepp, BenediktUDE
GND
1232038113
LSF ID
54109
ORCID
0000-0001-9059-5324ORCID iD
Other
connected with university
corresponding author
Open Access?:
OA Diamond
Scopus ID
Language of text:
English
Keyword, Topic:
Recommender systems ; Information filtering ; Conversational user interfaces ; Decision aids ; Evaluation ; User experience ; User studies ; User-centered design

Abstract in English:

Thus far, in most of the user experiments conducted in the area of recommender systems, the respective system is considered as an isolated component, i.e., participants can only interact with the recommender that is under investigation. This fails to recognize the situation of users in real-world settings, where the recommender usually represents only one part of a greater system, with many other options for users to find suitable items than using the mechanisms that are part of the recommender, e.g., liking, rating, or critiquing. For example, in current web applications, users can often choose from a wide range of decision aids, from text-based search over faceted filtering to intelligent conversational agents. This variety of methods, which may equally support users in their decision making, raises the question of whether the current practice in recommender evaluation is sufficient to fully capture the user experience. In this position paper, we discuss the need to take a broader perspective in future evaluations of recommender systems, and raise awareness for evaluation methods which we think may help to achieve this goal, but have not yet gained the attention they deserve.