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

TL;DR

This paper introduces a novel method for collecting user beliefs about unexperienced items on MovieLens, creating a dataset that helps analyze how beliefs influence consumer choices and improve recommender systems.

Contribution

The paper presents a new data collection approach for user beliefs about unexperienced items and provides a rich dataset for research on recommendation influence and user behavior.

Findings

Dataset includes user ratings, beliefs, and recommendations.

Identifies challenges like response bias and limited coverage.

Enables analysis of user choices without recommendations.

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/.