TY - GEN
T1 - The MovieLens Beliefs Dataset
T2 - 18th ACM Conference on Recommender Systems, RecSys 2024
AU - Aridor, Guy
AU - Gonçalves, Duarte
AU - Kong, Ruoyan
AU - Kluver, Daniel
AU - Konstan, Joseph A.
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2024/10/8
Y1 - 2024/10/8
N2 - An increasingly important aspect of designing recommender systems involves considering how recommendations will influence consumer choices. This paper addresses this issue by introducing a method for collecting user beliefs about un-experienced goods – a critical predictor of choice behavior. We implemented this method on the MovieLens platform, resulting in a rich dataset that combines user ratings, beliefs, and observed recommendations. We document challenges to such data collection, including selection bias in response and limited coverage of the product space. This unique resource empowers researchers to delve deeper into user behavior and analyze user choices absent recommendations, measure the effectiveness of recommendations, and prototype algorithms that leverage user belief data, ultimately leading to more impactful recommender systems. The dataset can be found at https://grouplens.org/datasets/movielens/ml_belief_2024/.
AB - An increasingly important aspect of designing recommender systems involves considering how recommendations will influence consumer choices. This paper addresses this issue by introducing a method for collecting user beliefs about un-experienced goods – a critical predictor of choice behavior. We implemented this method on the MovieLens platform, resulting in a rich dataset that combines user ratings, beliefs, and observed recommendations. We document challenges to such data collection, including selection bias in response and limited coverage of the product space. This unique resource empowers researchers to delve deeper into user behavior and analyze user choices absent recommendations, measure the effectiveness of recommendations, and prototype algorithms that leverage user belief data, ultimately leading to more impactful recommender systems. The dataset can be found at https://grouplens.org/datasets/movielens/ml_belief_2024/.
UR - http://www.scopus.com/inward/record.url?scp=85210478169&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85210478169&partnerID=8YFLogxK
U2 - 10.1145/3640457.3688158
DO - 10.1145/3640457.3688158
M3 - Conference contribution
AN - SCOPUS:85210478169
T3 - RecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems
SP - 855
EP - 860
BT - RecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems
PB - Association for Computing Machinery, Inc
Y2 - 14 October 2024 through 18 October 2024
ER -