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Enhancing trust, fairness, and performance in the sharing economy-algorithmic bias in the sharing economy: examining visibility disparities in Airbnb’s recommendation system
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Resumo(s)
This work project explores the factors shaping Airbnb host performance, visibility, and guest sentiment, focusing on algorithmic dynamics and predictive analytics. Using diverse datasets and Machine Learning models, it uncovers how Airbnb’s recommendation system impacts trust, competitiveness, and guest engagement. Key findings reveal that review volume and host responsiveness drive performance, with smaller hosts facing challenges in visibility and pricing. Sentiment analysis highlights the outsized impact of negative reviews on reputations. The study identifies biases in Airbnb’s algorithms, emphasizing the need for fairness, transparency, and user feedback to ensure equitable opportunities for all hosts in the sharing economy.
Descrição
Palavras-chave
Airbnb Machine learning Guest sentiment Algorithmic bias Host visibility Sharing economy Recommendation systems Review volume Predictive analytics Trust building