Ashmi Banerjee, M.Sc.

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
  • Winter 2023-24
  • Winter 2022-23
• Lab Course: Hands-on Recommender Systems
  • Summer 2024
  • Summer 2023

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

Got a thesis topic in mind that aligns with my research? Let me know!
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] Dynamic Playlist Generation for Hikers: An Intelligent Recommender System Using Trail Characteristics and Contextual Data
• [GR] Enhancing Sustainable Travel Recommendations Using Retrieval-Augmented Generation
• [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/BT] Understanding user trade-offs in travel decision-making

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

Recent Publications

An updated list of publications can be found on Google Scholar.

2024

• [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] Organisation Committee, ACM Women in RecSys Chapter
• [Since 2023] Google Developer Expert (GDE) in ML
• [Since 2022] Global Ambassador, Google Women Techmakers