The MovieLens Beliefs Dataset: Collecting Pre-Choice Data for Online Recommender Systems

• URL: http://arxiv.org/abs/2405.11053v3
• Date: Fri, 2 Aug 2024 13:26:44 GMT
• Title: The MovieLens Beliefs Dataset: Collecting Pre-Choice Data for Online Recommender Systems
• Authors: Guy Aridor, Duarte Goncalves, Ruoyan Kong, Daniel Kluver, Joseph Konstan,
• Abstract summary: This paper introduces a method for collecting user beliefs about unexperienced items - a critical predictor of choice behavior. We implement this method on the MovieLens platform, resulting in a rich dataset that combines user ratings, beliefs, and observed recommendations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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/.

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