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.

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

The Workshop on Recommenders in Tourism (RecTour) 2023, which is held in conjunction with the 17th issue of the ACM Conference on Recommender Systems (RecSys) in Singapore, addresses specific challenges for recommender systems in the tourism domain. In this overview paper, we summarize our motivations to organize the RecTour workshop and present the main topic areas of RecTour submissions. These include context-aware recommendations, group recommender systems, recommending composite items, decision making and user interaction issues, different information sources and various application scenarios.

The Workshop on Recommenders in Tourism (RecTour) 2022, which is held in conjunction with the 16th ACM Conference on Recommender Systems (RecSys), addresses specific challenges for recommender systems in the tourism domain. In this overview paper, we summarize our motivations to organize the RecTour workshop and present the main topic areas of RecTour submissions. These include context-aware recommendations, group recommender systems, recommending composite items, decision making and user interaction issues, different information sources and various application scenarios.

The Workshop on Recommenders in Tourism (RecTour) 2021, which is held in conjunction with the 15th ACM Conference on Recommender Systems (RecSys), addresses specific challenges for recommender systems in the tourism domain. In this overview paper, we summarize our motivations to organize the RecTour workshop and present the main topic areas of RecTour submissions. These include context-aware recommendations, group recommender systems, recommending composite items, decision making and user interaction issues, different information sources and various application scenarios.

Most recommender systems (RSs) predict the preferences of individual users; however, in certain scenarios, recommendations need to be made for a group of users. Tourism is a popular domain for group recommendations because people often travel in groups and look for point of interest (POI) sequences for their visits during a trip. In this study, we present different strategies that can be used to recommend POI sequences for groups. In addition, we introduce novel approaches, including a strategy called Split Group, which allows groups to split into smaller groups during a trip. We compared all strategies in a user study with 40 real groups. Our results proved that there was a significant difference in the quality of recommendations generated by using the different strategies. Most groups were willing to split temporarily during a trip, even when they were traveling with persons close to them. In this case, Split Group generated the best recommendations for different evaluation criteria. We use these findings to propose improvements for group recommendation strategies in the tourism domain.

The Workshop on Recommenders in Tourism (RecTour) 2019, which is held in conjunction with the 13th ACM Conference on Recommender Systems (RecSys), addresses specific challenges for recommender systems in the tourism domain. In this overview paper, we summarize our motivations to organize the RecTour workshop and present the main topics of the submissions that we received. The topics of this year's workshop include context-aware recommendations, group recommender systems, hotel recommendations, destination characterization, next-POI recommendation, user interaction and experience, preference elicitation, user modeling and application of machine learning algorithms in the context of tourism recommender systems.

In this demo paper, we present TourRec, amobile Recommender System (RS) for tourist trips, sequences of points of interest (POIs) along enjoyable routes. The core of TourRec is a modular, multi-tier architecture facilitating the development and evaluation of new recommendation algorithms, clients and data sources. We show how the TourRec Android application can be used to recommend tourist trips to individuals and groups. Furthermore, we explain how TourRec supports the evaluation of different recommendation algorithms and group recommendation strategies. A video demonstrating how TourRec works is available under https://youtu.be/o_yx8UGvvvo.

The Workshop on Recommenders in Tourism (RecTour) 2018, which is held in conjunction with the 12th ACM Conference on Recommender Systems (RecSys), addresses specific challenges for recommender systems within the tourism domain. In this paper, we summarize our motivations to organize this workshop and give an overview of the submissions that we received. The topics of this year's workshop include points-of-interest (POI), hotel and airline recommendations, recommending composite items such as POI sequences, group recommender systems, context-aware recommendation, decision making, user interaction issues, explanations and evaluation of tourism recommenders.

The Workshop on Recommenders in Tourism (RecTour) 2017, which is held in conjunction with the eleventh Conference on Recommender Systems (RecSys), addresses specific challenges for recommender systems within the tourism domain. In this overview, we summarize our motivations to organize such a workshop and give an overview of the submissions that we received. The main topics discussed in this year's workshop include context-aware recommender systems, group recommender systems, and the impact of itinerary, sequence, and trust on travel-related recommendations.

A proactive recommender system pushes recommendations to the user when the current situation seems appropriate, without explicit user request. This is conceivable in mobile scenarios such as restaurant or gas station recommendations. In this paper, we present a model for proactivity in mobile recommender systems. The model relies on domain-dependent context modeling in several categories. The recommendation process is divided into two phases to first analyze the current situation and then examine the suitability of particular items. We have implemented a prototype gas station recommender and conducted a survey for evaluation. Results showed good correlation of the output of our system with the assessment of users regarding the question when to generate recommendations.

Decentral recommender systems appear well suited for mobile scenarios, but have not been investigated thoroughly or implemented very much so far. We have designed and implemented a system to recommend images on personal digital assistants (PDAs). Our approach also incorporates recommending for groups of users that are present at a public shared display. The system exchanges rating vectors among PDAs, computes local matrices of item similarity and utilizes them to generate recommendations. Our innovation in comparison to existing systems includes improving the extensibility of the data model by introducing versioned rating vectors. In addition, we have optimized the storage requirements on the mobile device. We have evaluated the approach in a small user study. Furthermore, the scalability of our system was analyzed using a standard recommender data set resulting in positive findings.