CAPRI-FAIR: Integration of Multi-sided Fairness in Contextual POI Recommendation Framework

TL;DR

This paper introduces a post-filter method for POI recommendation systems that balances multi-sided fairness, including provider and consumer fairness, with recommendation accuracy, demonstrating effective tradeoffs through experiments.

Contribution

It proposes a novel post-filter approach integrating provider and consumer fairness into POI recommendation models, balancing fairness and performance.

Findings

Linear scoring for provider fairness balances exposure and accuracy.

Recommending popular POIs to inactive users improves precision.

Tradeoffs exist between fairness and precision depending on model and dataset.

Abstract

Point-of-interest (POI) recommendation considers spatio-temporal factors like distance, peak hours, and user check-ins. Given their influence on both consumer experience and POI business, it's crucial to consider fairness from multiple perspectives. Unfortunately, these systems often provide less accurate recommendations to inactive users and less exposure to unpopular POIs. This paper develops a post-filter method that includes provider and consumer fairness in existing models, aiming to balance fairness metrics like item exposure with performance metrics such as precision and distance. Experiments show that a linear scoring model for provider fairness in re-scoring items offers the best balance between performance and long-tail exposure, sometimes without much precision loss. Addressing consumer fairness by recommending more popular POIs to inactive users increased precision in some models and datasets. However, combinations that reached the Pareto front of consumer and provider fairness resulted in the lowest precision values, highlighting that tradeoffs depend greatly on the model and dataset.