1. Evaluating Music Recommender Systems for Groups
- Author
-
Mezei, Zsolt and Eickhoff, Carsten
- Subjects
Computer Science - Artificial Intelligence ,Computer Science - Human-Computer Interaction ,Computer Science - Information Retrieval - Abstract
Recommendation to groups of users is a challenging and currently only passingly studied task. Especially the evaluation aspect often appears ad-hoc and instead of truly evaluating on groups of users, synthesizes groups by merging individual preferences. In this paper, we present a user study, recording the individual and shared preferences of actual groups of participants, resulting in a robust, standardized evaluation benchmark. Using this benchmarking dataset, that we share with the research community, we compare the respective performance of a wide range of music group recommendation techniques proposed in the, Comment: Presented at the 2017 Workshop on Value-Aware and Multistakeholder Recommendation
- Published
- 2017