Evaluating Music Recommender Systems for Groups
Artificial Intelligence
2017-08-01 v1 Human-Computer Interaction
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
Cite
@article{arxiv.1707.09790,
title = {Evaluating Music Recommender Systems for Groups},
author = {Zsolt Mezei and Carsten Eickhoff},
journal= {arXiv preprint arXiv:1707.09790},
year = {2017}
}
Comments
Presented at the 2017 Workshop on Value-Aware and Multistakeholder Recommendation