Work

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

Year: 2024

Type: conference-paper

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 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/.

Source: ACM Conference on Recommender Systems (RecSys)

DOI: 10.1145/3640457.3688158

Authors: Guy Aridor, Duarte Goncalves, Ruoyan Kong, Daniel Kluver, Joseph Konstan

Institutions: Northwestern University, University College London, University of Minnesota

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4403219783

FWCI: 1.153

Cites: 17

Cited by: 3

Related to: 10