Evaluating Music Recommender Systems for Groups

Autor:	Mezei, Zsolt, Eickhoff, Carsten
Rok vydání:	2017
Předmět:	Computer Science - Artificial Intelligence Computer Science - Human-Computer Interaction Computer Science - Information Retrieval
Druh dokumentu:	Working Paper
Popis:	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
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1707.09790 Zobrazit plný text záznamu View this record from Arxiv