Reconciling the accuracy-diversity trade-off in recommendations

TL;DR

This paper explains the accuracy-diversity trade-off in recommendation systems through user consumption constraints, showing that standard accuracy metrics overlook these constraints and affect diversity outcomes.

Contribution

It introduces a stylized model linking consumption constraints to recommendation diversity, providing a new theoretical understanding of the trade-off.

Findings

Objectives considering consumption constraints promote diversity.

Standard accuracy metrics overlook user consumption limits.

The model offers practical insights for designing diverse recommendations.

Abstract

In recommendation settings, there is an apparent trade-off between the goals of accuracy (to recommend items a user is most likely to want) and diversity (to recommend items representing a range of categories). As such, real-world recommender systems often explicitly incorporate diversity separately from accuracy. This approach, however, leaves a basic question unanswered: Why is there a trade-off in the first place? We show how the trade-off can be explained via a user's consumption constraints -- users typically only consume a few of the items they are recommended. In a stylized model we introduce, objectives that account for this constraint induce diverse recommendations, while objectives that do not account for this constraint induce homogeneous recommendations. This suggests that accuracy and diversity appear misaligned because standard accuracy metrics do not consider consumption constraints. Our model yields precise and interpretable characterizations of diversity in different settings, giving practical insights into the design of diverse recommendations.