6 papers
Collab-REC: An LLM-based Agentic Framework for Balancing Recommendations in Tourism
Ashmi Banerjee, Adithi Satish, Fitri Nur Aisyah +3
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…
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…
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…
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,…
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…
Tuning Into Bias: A Computational Study of Gender Bias in Song Lyrics
Danqing Chen, Adithi Satish, Rasul Khanbayov +2
The application of text mining methods is becoming increasingly prevalent, particularly within Humanities and Computational Social Sciences, as well as in a broader range of discip…