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Recommender systems and user modeling

Student Projects in Recommender systems and user modeling

Contacts (if not stated otherwise): Markus Schedl, Christine Bauer

These student projects can be started any time (including holidays) and can span over semester boundaries.

Remark: We are open for new proposals - if you are interested in Recommender Systems and User Modeling , feel free to contact us!

 

Topics:

  • Fairness in recommender systems: metrics, tradeoffs, and countermeasures (Christine Bauer)
  • Investigating (un-)fairness in performance and diversity measures of recommender systems algorithms (Markus Schedl)
  • Investigating bias of recommender systems algorithms (Markus Schedl)
  • Investigating bias of recommender systems datasets (Markus Schedl)
  • Explainable recommendations: transparency and justification (Markus Schedl)
  • Using recurrent neural networks for music playlist continuation (Markus Schedl)
  • Graph embeddings for recommendation in online social networks (Markus Schedl)
  • Privacy-protecting recommender systems (Markus Schedl)
  • Integrating personality information into a movie recommendation system (Markus Schedl)
  • State of the art in situation-aware recommender systems (Christine Bauer)
  • Content-based recommender systems for audio, image, video (Markus Schedl)
  • Psychology-inspired recommender systems (Markus Schedl)
  • Recommender systems for music recommendation (Christine Bauer, Markus Schedl)
  • Recommender systems for point-of-interest recommendation (Markus Schedl
  • Recommender systems for hotel/accommodation recommendation (Markus Schedl)
  • Recommender systems for job recommendation (Markus Schedl)
  • Recommender systems for fashion recommendation (Markus Schedl)
  • Recommender systems for recommending content or users in online social networks (Markus Schedl)