Prof. Dr. Felix Naumann

Maximilian Jenders

former member


Research Interests

  • Web Mining
  • Opinion Mining
  • Text Mining
  • Newspaper Text Analysis
  • Text Recommendation
  • Social Media Analysis
  • Machine Learning
  • Data Mining
  • MOOC courses


Co-Supervised Master's Theses:

  • Lukas Schulze: Profiling Log Messages For Unknown Error Detection (finished, 2015)
  • Tobias Schubotz: Online Temporal Summarization of News Articles (finished, 2014)
  • Mandy Roick: A Topic-Based Search for Microblog Posts (finished, 2014)
  • Thorben Lindhauer: A Content-Based Serendipity Model for News Recommendation (finished, 2014)



  • Which Answer is Best? Predicting Accepted Answers in MOOC Forums. Jenders, Maximilian; Krestel, Ralf; Naumann, Felix (2016). 679–684.
  • A Serendipity Model For News Recommendation. Jenders, Maximilian; Lindhauer, Thorben; Kasneci, Gjergji; Krestel, Ralf; Naumann, Felix in Lecture Notes in Computer Science (2015). (Vol. 9324) 111–123.
  • How to Stay Up-to-date on Twitter with General Keywords. Roick, Mandy; Jenders, Maximilian; Krestel, Ralf in CEUR Workshop Proceedings (2015). (Vol. 1458)
  • Analyzing and Predicting Viral Tweets. Jenders, Maximilian; Kasneci, Gjergji; Naumann, Felix (2013).
  • Ein Datenbankkurs mit 6000 Teilnehmern - Erfahrungen auf der openHPI MOOC Plattform. Naumann, Felix; Jenders, Maximilian; Papenbrock, Thorsten in Informatik-Spektrum (2013). 37(12) 333–340.