CAPRI-FAIR: Integration of Multi-sided Fairness in Contextual POI Recommendation Framework
<|reference_start|>CAPRI-FAIR: Integration of Multi-sided Fairness in Contextual POI Recommendation Framework: 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.<|reference_end|>
@article{cruz2024capri-fair:,
    title={CAPRI-FAIR: Integration of Multi-sided Fairness in Contextual POI
  Recommendation Framework},
    author={Francis Zac dela Cruz, Flora D. Salim, Yonchanok Khaokaew, Jeffrey
  Chan},
    journal={arXiv preprint arXiv:2406.03109},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03109},
    primaryClass={cs.IR}
}