Towards creating a non-synthetic group recommendation dataset

Towards creating a non-synthetic group recommendation dataset

Rijlaarsdam, Matthijs (Student TU Delft)
Scholten, Sebastiaan (Student TU Delft)
Liem, C.C.S. (TU Delft Multimedia Computing)

Shalom, Oren Sar (editor)
Jannach, Dietmar (editor)
Guy, Ido (editor)

2019

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.

Crowd sourcing
Datasets
Group recommendation

http://resolver.tudelft.nl/uuid:7c9bd078-ab50-4f9b-a94d-cc4322f8acfa

CEUR-WS.org

ImpactRS 2019 Impact of Recommender Systems 2019: Proceedings of the 1st Workshop on the Impact of Recommender Systems co-located with 13th ACM Conference on Recommender Systems (ACM RecSys 2019)

1st Workshop on the Impact of Recommender Systems, ImpactRS 2019, 2019-09-19, Copenhagen, Denmark

CEUR Workshop Proceedings, 1613-0073, 2462

Institutional Repository

conference paper

© 2019 Matthijs Rijlaarsdam, Sebastiaan Scholten, C.C.S. Liem