Traditional conversational travel recommender systems primarily optimize for user relevance and convenience, often reinforcing popular, overcrowded destinations and carbon-intensive travel choices. To address this, we present TRACE (Tourism Recommendation with Agentic Counterfactual Explanations), a multi-agent, LLM-based framework that promotes sustainable tourism through interactive nudging. TRACE uses a modular orchestrator-worker architecture where specialized agents elicit latent sustainability preferences, construct structured user personas, and generate recommendations that balance relevance with environmental impact. A key innovation lies in its use of agentic counterfactual explanations and LLM-driven clarifying questions, which together surface greener alternatives and refine understanding of intent, fostering user reflection without coercion. User studies and semantic alignment analyses demonstrate that TRACE effectively supports sustainable decision-making while preserving recommendation quality and interactive responsiveness. TRACE is implemented on Google's Agent Development Kit, with full code, Docker setup, prompts, and a publicly available demo video to ensure reproducibility. A project summary, including all resources, prompts, and demo access, is available at https://ashmibanerjee.github.io/trace-chatbot.

Evaluating nuanced conversational travel recommendations is challenging when human annotations are costly and standard metrics ignore stakeholder-centric goals. We study LLMs-as-Judges for sustainable city-trip lists across four dimensions -- relevance, diversity, sustainability, and popularity balance, and propose a three-phase calibration framework: (1) baseline judging with multiple LLMs, (2) expert evaluation to identify systematic misalignment, and (3) dimension-specific calibration via rules and few-shot examples. Across two recommendation settings, we observe model-specific biases and high dimension-level variance, even when judges agree on overall rankings. Calibration clarifies reasoning per dimension but exposes divergent interpretations of sustainability, highlighting the need for transparent, bias-aware LLM evaluation. Prompts and code are released for reproducibility: https://github.com/ashmibanerjee/trs-llm-calibration.

Large Language Models (LLMs) are transforming personalized services by enabling adaptive, context-aware recommendations and interactions. However, deploying these models at scale raises significant concerns about environmental impact, fairness, privacy, and trustworthiness, including high energy consumption, biased outputs, privacy breaches, and hallucinations. The LLM4Good workshop was already hosted at UMAP’25 and is a half-day workshop that addresses these challenges by fostering dialogue on sustainable and ethical approaches to LLM-based personalization. Participants will explore energy-efficient techniques, bias mitigation, privacy-preserving methods, and responsible deployment strategies. The workshop aligns with Sustainable Development Goals and Digital Humanism principles. It aims to guide the development of trustworthy, human-centric LLM systems that positively impact education, healthcare, and other domains.

Recommender Systems (RS) influence everyday decisions, yet most remain optimized for short-term engagement or commercial gain. RS4SD aims to shift this focus by exploring how RS can contribute to sustainable development through behavioral change and nudging strategies. Aligned with the UN Sustainable Development Goals (SDG), RS4SD will highlight applications that promote responsible consumption, sustainable mobility, healthy eating, and digital well-being. In particular, we will focus on how AI and RS can be designed to foster sustainable behaviors through multi-objective optimization and ethically aligned interventions. These objectives are directly tied to the UN SDG, and we welcome all contributions showcasing RS in support of these goals. A central theme of the workshop is the integration of behavioral science and AI to design interventions that guide users toward more sustainable and healthier choices while preserving individual autonomy. Topics of interest include multi-objective recommendation, health-aware RS, eco-friendly product and tourism RS, as well as novel evaluation metrics that go beyond accuracy to capture societal impact. RS4SD will bring together researchers, stakeholders and practitioners from RS, AI, sustainability, and behavioral science to share models, datasets, frameworks, and real-world use cases. The workshop encourages interdisciplinary collaboration and aims to build a community dedicated to responsible, behavior-aware RS that benefit both individuals and society.

Tourism Recommender Systems (TRS) are crucial in personalizing travel experiences by tailoring recommendations to users' preferences, constraints, and contextual factors. However, publicly available travel datasets often lack sufficient breadth and depth, limiting their ability to support advanced personalization strategies - particularly for sustainable travel and off-peak tourism. In this work, we explore using Large Language Models (LLMs) to generate synthetic travel queries that emulate diverse user personas and incorporate structured filters such as budget constraints and sustainability preferences. This paper introduces a novel SynthTRIPs framework for generating synthetic travel queries using LLMs grounded in a curated knowledge base (KB). Our approach combines persona-based preferences (e.g., budget, travel style) with explicit sustainability filters (e.g., walkability, air quality) to produce realistic and diverse queries. We mitigate hallucination and ensure factual correctness by grounding the LLM responses in the KB. We formalize the query generation process and introduce evaluation metrics for assessing realism and alignment. Both human expert evaluations and automatic LLM-based assessments demonstrate the effectiveness of our synthetic dataset in capturing complex personalization aspects underrepresented in existing datasets. While our framework was developed and tested for personalized city trip recommendations, the methodology applies to other recommender system domains. Code and dataset are made public at https://bit.ly/synthTRIPs

In the travel industry, Tourism Recommender Systems (TRS) are gaining popularity as they simplify trip planning for travelers by offering personalized recommendations for accommodations, activities, destinations, and more. Ensuring fairness in TRS involves considering the needs and viewpoints of different stakeholders, including consumers, item providers, the platform, and society. Although previous research has focused on fairness in TRS from a multistakeholder perspective, little attention has been given to generating sustainable recommendations.

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 search in the form of a nearby feature to query for hotels or restaurants in the vicinity. Moreover, hotel booking platforms like Booking[dot]com, Expedia, or Trivago allow travelers searching for accommodations using either their desired location as a search query or near a particular landmark. Since the popularity of different locations in a city varies, certain locations may get more queries than other locations. Thus, the exposure received by different establishments at these locations may be very different from their intrinsic quality as captured in their ratings.Today, many small businesses (shops, hotels, or restaurants) rely on such online platforms for attracting customers. Thus, receiving less exposure than what is expected can be unfavorable for businesses. It could have a negative impact on their revenue and potentially lead to economic starvation or even shutdown. By gathering and analyzing data from three popular platforms, we observe that many top-rated hotels and restaurants get less exposure vis-a-vis their quality, which could be detrimental for them. Following a meritocratic notion, we define and quantify such exposure disparity due to location-based searches on these platforms. We attribute this exposure disparity mainly to two kinds of biases - Popularity Bias and Position Bias. Our experimental evaluation on multiple datasets reveals that although the platforms are doing well in delivering distance-based results, exposure disparity exists for individual businesses and needs to be reduced for business sustainability.

The COVID-19 pandemic has made it paramount to maintain social distance to limit the viral transmission probability. At the same time, local businesses (e.g., restaurants, cafes, stores, malls) need to operate to ensure their economic sustainability. Considering the wide usage of local recommendation platforms like Google Local and Yelp by customers to choose local businesses, we propose to design local recommendation systems which can help in achieving both safety and sustainability goals. Our investigation of existing local recommendation systems shows that they can lead to overcrowding at some businesses compromising customer safety, and very low footfall at other places threatening their economic sustainability. On the other hand, naive ways of ensuring safety and sustainability can cause significant loss in recommendation utility for the customers. Thus, we formally express the problem as a multi-objective optimization problem and solve by innovatively mapping it to a bipartite matching problem with polynomial time solutions. Extensive experiments over multiple real-world datasets reveal the efficacy of our approach along with the three-way control over sustainability, safety, and utility goals.