activity
20242026
most citedSynthTRIPs: A Knowledge-Grounded Framework for Benchmark Query Generation for Personalized Tourism Recommenders

4 citations · 4 across the 3 of their papers we have counts for

collaborators

5 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.IR20254 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

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.CL2024

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…