TY - GEN
T1 - Towards creating a non-synthetic group recommendation dataset
AU - Rijlaarsdam, Matthijs
AU - Scholten, Sebastiaan
AU - Liem, Cynthia C.S.
PY - 2019
Y1 - 2019
N2 - Recommender systems can be useful in group settings, e.g. when choosing a movie to watch with a group. However, while considerable research in group recommendation has been performed, we still lack truly ecological datasets on group recommendations in real life consumption scenarios. Much of the existing work considers hypothetical consumption scenarios, and commonly, individual ratings are aggregated, but no actual group consumption takes place in which situational differences per group are taken into account. In this paper, we outline a vision for acquiring more realistic and ecological group consumption data, based on a crowdsourcing application that will acquire individual ratings per group consumption event. We discuss various design decisions that will allow us to gather these ratings effectively from a large group of people, and demonstrate and evaluate the viability of our approach towards reaching group consensus through rating session simulations.
AB - Recommender systems can be useful in group settings, e.g. when choosing a movie to watch with a group. However, while considerable research in group recommendation has been performed, we still lack truly ecological datasets on group recommendations in real life consumption scenarios. Much of the existing work considers hypothetical consumption scenarios, and commonly, individual ratings are aggregated, but no actual group consumption takes place in which situational differences per group are taken into account. In this paper, we outline a vision for acquiring more realistic and ecological group consumption data, based on a crowdsourcing application that will acquire individual ratings per group consumption event. We discuss various design decisions that will allow us to gather these ratings effectively from a large group of people, and demonstrate and evaluate the viability of our approach towards reaching group consensus through rating session simulations.
KW - Crowd sourcing
KW - Datasets
KW - Group recommendation
UR - http://www.scopus.com/inward/record.url?scp=85073513927&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85073513927
T3 - CEUR Workshop Proceedings
SP - 1
EP - 5
BT - ImpactRS 2019 Impact of Recommender Systems 2019
A2 - Shalom, Oren Sar
A2 - Jannach, Dietmar
A2 - Guy , Ido
PB - CEUR-WS
T2 - 1st Workshop on the Impact of Recommender Systems, ImpactRS 2019
Y2 - 19 September 2019 through 19 September 2019
ER -