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

• WS 23/24: Master Seminar: Topics in Recommender Systems
• SS 2023: Lab Course: Hands-on Recommender Systems
• WS 22/23: Master Seminar: Topics in Recommender Systems

Theses Topics

No theses available at the moment. Please check again later!
When applying for a topic please follow the guidelines from here.

Legend: [BT] = Bachelor Thesis; [MT] = Master Thesis;  [GR] = Guided Research; [AP] = Application Project

Open Topics:

No theses available at the moment. Please check again later!
Other theses and research topics are listed here. 

Completed:

• [MT]: Explaining Fairness in Multi-stakeholder Recommender Systems for Tourism
• [BT]: User Interfaces for Combining Multiple Items in Tourism Recommender Systems

In Progress:

• [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]: Exploring Substitution Recommendations: A Literature Review and Analysis
• [BT/MT]: Design and Development of a Web Frontend for Sustainable Hike Recommendations
• [BT]: Analyzing Mobility Trends for Travel Behavior, Preferences, and Sustainability Concerns
• [MT]: User Traits and Responsible Travel Decision Making in Recommender Systems
• [MT]: Evaluating User Decision-Making in Responsible Tourism: A Green Destination Recommender

Recent Publications

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

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] Machine Learning GDE, Google Developer Expert
• [Since 2022] Global Ambassador, Google Women Techmakers