TY - GEN
T1 - Green Destination Recommender
T2 - 32nd ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2024
AU - Banerjee, Ashmi
AU - Mahmudov, Tunar
AU - Wörndl, Wolfgang
N1 - Publisher Copyright:
© 2024 Owner/Author.
PY - 2024/6/27
Y1 - 2024/6/27
N2 - Tourism Recommender Systems (TRS) have evolved from only providing user recommendations to becoming convergence points for multiple stakeholders. This necessitates recognizing the interests of all stakeholders, particularly in the tourism sector, which faces challenges like seasonality and resource constraints. Our stakeholder classification identifies consumers, item providers, platform, and society as key stakeholders, highlighting the complexity of real-world relationships. Fairness in TRS demands a multistakeholder approach, integrating sustainability to address the broader societal impact. While research has focused on fair recommendation systems in tourism, the focus on generating sustainable recommendations remains limited. This demo paper aims to enhance fairness in TRS, mainly focusing on society as a stakeholder. We introduce the Green Destination Recommender (GDR), an application that prioritizes Societal Fairness (S-Fairness) by encouraging environmentally conscious decisions. GDR recommends sustainable tourism destinations based on the user's starting location, travel month, and specific interests. The application promotes ecologically friendly options by recommending less popular yet appealing destinations, considering the emissions from transport to reach the destination and the seasonal demand to balance visitor numbers year-round.
AB - Tourism Recommender Systems (TRS) have evolved from only providing user recommendations to becoming convergence points for multiple stakeholders. This necessitates recognizing the interests of all stakeholders, particularly in the tourism sector, which faces challenges like seasonality and resource constraints. Our stakeholder classification identifies consumers, item providers, platform, and society as key stakeholders, highlighting the complexity of real-world relationships. Fairness in TRS demands a multistakeholder approach, integrating sustainability to address the broader societal impact. While research has focused on fair recommendation systems in tourism, the focus on generating sustainable recommendations remains limited. This demo paper aims to enhance fairness in TRS, mainly focusing on society as a stakeholder. We introduce the Green Destination Recommender (GDR), an application that prioritizes Societal Fairness (S-Fairness) by encouraging environmentally conscious decisions. GDR recommends sustainable tourism destinations based on the user's starting location, travel month, and specific interests. The application promotes ecologically friendly options by recommending less popular yet appealing destinations, considering the emissions from transport to reach the destination and the seasonal demand to balance visitor numbers year-round.
KW - Multistakeholder Fairness
KW - Societal Fairness
KW - Sustainability
KW - Tourism Recommender Systems
UR - http://www.scopus.com/inward/record.url?scp=85198970728&partnerID=8YFLogxK
U2 - 10.1145/3631700.3664909
DO - 10.1145/3631700.3664909
M3 - Conference contribution
AN - SCOPUS:85198970728
T3 - UMAP 2024 - Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization
SP - 486
EP - 490
BT - UMAP 2024 - Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization
PB - Association for Computing Machinery, Inc
Y2 - 1 July 2024 through 4 July 2024
ER -