Ashmi Banerjee, M.Sc.
Email: | ashmi.banerjee[at]tum[dot]de |
Office: | 01.05.053 |
Website: | ashmibanerjee.com |
Address:
| TUM School of Computation, Information and Technoloy |
I am a Ph.D. candidate at the Chair of Connected Mobility at the Technical University of Munich (TUM), from where I also obtained my M.Sc. in Informatics in 2019.
Research Interests
- Multistakeholder Recommender Systems
- Human-Computer Interaction
- Data Mining, Information Retrieval
- Machine Learning
- Fairness & Responsible Recommendations
I am working together with Dr. Wolfgang Wörndl.
Teaching
- Master Seminar: Topics in Recommender Systems
- Lab Course: Hands-on Recommender Systems
Theses Topics
When applying for a topic please follow the guidelines from here.
The thesis students starter pack for (potential) students supervised by me can be found here.
Legend: [BT] = Bachelor Thesis; [MT] = Master Thesis; [GR] = Guided Research; [AP] = Application Project
No theses available at the moment. Please check again later!
Other theses and research topics are listed here.
Please use your @tum.de or @mytum.de or similar emails for any communication. Otherwise, your emails might end up in spam and never get answered!
In Progress
- [MT] Exploring Alternative Travel Destinations: A Metric-Constrained Approach Based on City Interest Profiles
- [BT] Exploring the Applicability and Implications of Large Language Models for Tourism Recommendation
- [MT] Exploring Trade-offs in Travel Planning: Prioritizing Sustainability While Managing Cost and Attractiveness through User-Centric Design
- [MT] Gamification for Sustainable City Trip Planning
- [BT] Balancing Sustainability and Experience: Analyzing User Trade-offs in Digital Trip Planning Tools
- [BT] An Exploration of Hybrid Retrieval Augmented Generation in Conversational Tourism Recommender Systems
- [MT] Balancing Sustainability, Cost, and Appeal in Tourism Recommender Systems: A User-Centric Approach
- [BT] Analyzing the Effects of Visualization Strategies on Sustainable Decision-Making in Tourism Recommender Systems
Completed
- [MT] Explaining Fairness in Multi-stakeholder Recommender Systems for Tourism
- [BT] User Interfaces for Combining Multiple Items in Tourism Recommender Systems
- [MT] An analysis of long-distance train delays and impacted stations in Germany
- [BT] Exploring Data-Driven Approaches for Train Delay Prediction using Multivariate Models and Machine Learning Techniques
- [GR] Multi-objective Optimization for Fairness in Tourism Recommender Systems
- [MT] User Traits and Responsible Travel Decision-Making in Recommender Systems
- [MT] Evaluating User Decision-Making in Responsible Tourism: A Green Destination Recommender
- [MT] Exploring Substitution Recommendations: A Literature Review and Analysis
- [BT/MT] Design and Development of a Web Frontend for Sustainable Hike Recommendations
- [GR] Analyzing Responsibility Features in Tourism
- [BT] Analyzing Mobility Trends for Travel Behavior, Preferences, and Sustainability Concerns
- [MT] Dynamic Playlist Generation for Hikers: An Intelligent Recommender System Using Trail Characteristics and Contextual Data
- [GR] Enhancing Sustainable Travel Recommendations Using Retrieval-Augmented Generation
Recent Publications
An updated list of publications can be found on Google Scholar.
2024
-
[Workshop Paper]
Ashmi Banerjee*, Adithi Satish*, and Wolfgang Wörndl, 2024. Enhancing Tourism Recommender Systems for Sustainable City Trips Using Retrieval-Augmented Generation. Accepted for publication at the 1st International Workshop on Recommender Systems for Sustainability and Social Good (RecSoGood), co-located with ACM RecSys 2024. https://arxiv.org/abs/2409.18003 -
[Workshop Paper]
Ashmi Banerjee*, Tunar Mahmudov*, and Wolfgang Wörndl, 2024. Green Destination Recommender: A Web Application to Encourage Responsible City Trip Recommendations. In Adjunct Proceedings of the 32nd ACM Con- ference on User Modeling, Adaptation and Personalization (UMAP Adjunct ’24), July 01–04, 2024, Cagliari, Italy. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3631700.3664909 -
[arXiv preprint]
Ashmi Banerjee, Tunar Mahmudov, Emil Adler, Fitri Nur Aisyah, Wolfgang Wörndl, 2024. Modeling Sustainable City Trips: Integrating CO2 Emissions, Popularity, and Seasonality into Tourism Recommender Systems. arXiv preprint arXiv:2403.18604 (2024). -
[arXiv preprint]
Ashmi Banerjee*, Tunar Mahmudov*, and Wolfgang Wörndl, 2024. A User Interface Study on Sustainable City Trip Recommendations. arXiv preprint arXiv:2405.11243 (2024).
2023
- [Workshop Position Paper]
Ashmi Banerjee, Paromita Banik, and Wolfgang Wörndl, 2023. Towards Individual and Multistakeholder Fairness in Tourism Recommender Systems. In Proceedings of the 6th FAccTRec Workshop on Responsible Recommendation (FAccTRec `23) co-located with the 16th ACM Conference on Recommender Systems (RecSys), September 18-22, 2023, Singapore. ACM, New York, NY, USA.
PDF | BibTeX
- [Workshop Paper]
Paromita Banik, Ashmi Banerjee, and Wolfgang Wörndl. 2023. Understanding User Perspectives on Sustainability and Fairness in Tourism Recommender Systems. In UMAP ’23 Adjunct: Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’23 Adjunct), June 26–29, 2023, Limassol, Cyprus. ACM, New York, NY, USA, 8 pages.
PDF | BibTeX
- [Doctoral Consortium]
Ashmi Banerjee. 2023. Fairness and Sustainability in Multistakeholder Tourism Recommender Systems. In UMAP ’23: Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’23), June 26–29, 2023, Limassol, Cyprus. ACM, New York, NY, USA, 6 pages.
PDF | BibTeX
- [Journal Paper]
Banerjee, A., Banik, P., & Wörndl, W. A Review on Individual and Multistakeholder Fairness in Tourism Recommender Systems. Frontiers in Big Data, 6.
PDF | BibTeX
2020
-
[Workshop Paper]
Ashmi Banerjee, Gourab K Patro, Linus W. Dietz, Abhijnan Chakraborty, Analyzing ‘Near Me’ Services: Potential for Exposure Bias in Location-based Retrieval, International Workshop on Fair and Interpretable Learning Algorithms (FILA 2020) in conjunction with the IEEE International Conference on Big Data (IEEE BigData 2020)
PDF | BibTeX -
[Conference Paper]
Gourab K Patro, Abhijnan Chakraborty, Ashmi Banerjee, Niloy Ganguly, Towards Sustainability and Safety: Designing Local Recommendations for Post-pandemic World, 14th ACM Conference on Recommender Systems (RecSys 2020)
PDF | BibTeX
Miscellaneous
- [Since 2023] Google Developer Expert (GDE) in ML
- [2023-2024] Organisation Committee, ACM Women in RecSys Chapter
- [Since 2022] Global Ambassador, Google Women Techmakers