Evaluating Music Recommender Systems for Groups
This addresses the problem of ad-hoc and synthetic evaluation methods for group recommender systems in music, providing a standardized benchmark for researchers, though it is incremental as it focuses on evaluation rather than new recommendation methods.
The paper tackled the challenge of evaluating music recommender systems for groups by conducting a user study to record individual and shared preferences of actual groups, resulting in a robust, standardized evaluation benchmark. They compared the performance of various music group recommendation techniques using this dataset, which they shared with the research community.
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