activity
20202026
most citedTowards Individual and Multistakeholder Fairness in Tourism Recommender Systems

32 citations · 73 across the 9 of their papers we have counts for

collaborators

10 papers

cs.AI2026

Multi-Dimensional Evaluation of Sustainable City Trips with LLM-as-a-Judge and Human-in-the-Loop

Ashmi Banerjee, Adithi Satish, Wolfgang Wörndl +1

Evaluating nuanced conversational travel recommendations is challenging when human annotations are costly and standard metrics ignore stakeholder-centric goals. We study LLMs-as-Ju…

cs.IR2026

TRACE: A Conversational Framework for Sustainable Tourism Recommendation with Agentic Counterfactual Explanations

Ashmi Banerjee, Adithi Satish, Wolfgang Wörndl +1

Traditional conversational travel recommender systems primarily optimize for user relevance and convenience, often reinforcing popular, overcrowded destinations and carbon-intensiv…

cs.HC2025

SmartSustain Recommender System: Navigating Sustainability Trade-offs in Personalized City Trip Planning

Ashmi Banerjee, Melih Mert Aksoy, Wolfgang Wörndl

Tourism is a major contributor to global carbon emissions and over-tourism, creating an urgent need for recommender systems that not only inform but also gently steer users toward…

cs.AI2025★ 1 cited

Collab-REC: An LLM-based Agentic Framework for Balancing Recommendations in Tourism

Ashmi Banerjee, Adithi Satish, Fitri Nur Aisyah +2

We propose COLLAB-REC, a multi-agent framework designed to counteract popularity bias and improve diversity in tourism recommendations. In our setup, three LLM-based agents(Persona…

cs.IR2025★ 4 cited

SynthTRIPs: A Knowledge-Grounded Framework for Benchmark Query Generation for Personalized Tourism Recommenders

Ashmi Banerjee, Adithi Satish, Fitri Nur Aisyah +2

Tourism Recommender Systems (TRS) are crucial in personalizing travel experiences by tailoring recommendations to users' preferences, constraints, and contextual factors. However,…

cs.IR2024★ 16 cited

Enhancing Tourism Recommender Systems for Sustainable City Trips Using Retrieval-Augmented Generation

Ashmi Banerjee, Adithi Satish, Wolfgang Wörndl

Tourism Recommender Systems (TRS) have traditionally focused on providing personalized travel suggestions, often prioritizing user preferences without considering broader sustainab…

cs.HC2024★ 1 cited

A User Interface Study on Sustainable City Trip Recommendations

Ashmi Banerjee, Tunar Mahmudov, Wolfgang Wörndl

The importance of promoting sustainable and environmentally responsible practices is becoming increasingly recognized in all domains, including tourism. The impact of tourism exten…

cs.IR2024★ 19 cited

Modeling Sustainable City Trips: Integrating CO2e Emissions, Popularity, and Seasonality into Tourism Recommender Systems

Ashmi Banerjee, Tunar Mahmudov, Emil Adler +2

Tourism affects not only the tourism industry but also society and stakeholders such as the environment, local businesses, and residents. Tourism Recommender Systems (TRS) can be p…

cs.IR2023★ 32 cited

Towards Individual and Multistakeholder Fairness in Tourism Recommender Systems

Ashmi Banerjee, Paromita Banik, Wolfgang Wörndl

This position paper summarizes our published review on individual and multistakeholder fairness in Tourism Recommender Systems (TRS). Recently, there has been growing attention to…

cs.IR2020

Analyzing 'Near Me' Services: Potential for Exposure Bias in Location-based Retrieval

Ashmi Banerjee, Gourab K Patro, Linus W. Dietz +1

The proliferation of smartphones has led to the increased popularity of location-based search and recommendation systems. Online platforms like Google and Yelp allow location-based…