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Research Details
The MovieLens Beliefs Dataset: Collecting Pre-Choice Data for Online Recommender Systems
Abstract
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 items – 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/.
Type
Working Paper
Author(s)
Guy Aridor, Duarte Goncalves, Daniel Kluver, Ruoyan Kong, Joseph Konstan
Date Published
2024
Citations
Aridor, Guy, Duarte Goncalves, Daniel Kluver, Ruoyan Kong, and Joseph Konstan. 2024. The MovieLens Beliefs Dataset: Collecting Pre-Choice Data for Online Recommender Systems.