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The MovieLens Beliefs Dataset: Collecting Pre-Choice Data for Online Recommender Systems
An increasingly important aspect of designing recommender systems involves considering how recommendations will influence consumer choices.
The Economics of Recommender Systems: Evidence from a Field Experiment on MovieLens
We conduct a field experiment on a movie-recommendation platform to identify if and how recommendations affect consumption.
Competing Bandits: The Perils of Exploration Under Competition
Users arrive one by one and choose between the two firms, so that each firm makes progress on its bandit problem only if it is chosen.
Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems
We study a model of user decision-making in the context of recommender systems via numerical simulation.
The Perils of Exploration under Competition: A Computational Modeling Approach
We empirically study the interplay between exploration and competition.
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