The MovieLens Beliefs Dataset: Collecting Pre-Choice Data for Online Recommender Systems
This provides a resource for researchers to analyze user behavior and improve recommender systems, but it is incremental as it focuses on data collection rather than new algorithms.
The paper tackles the problem of understanding how recommendations influence consumer choices by introducing a method to collect user beliefs about un-experienced items, resulting in the MovieLens Beliefs Dataset that combines ratings, beliefs, and recommendations.
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/.