Prof. Dr. Felix Naumann

Maximilian Jenders

für Softwaresystemtechnik
Prof.-Dr.-Helmert-Straße 2-3
D-14482 Potsdam

Phone: +49 331 5509 289
Fax: +49 331 5509 287
Room: E-2.01-2
Email:  Maximilian Jenders


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)




A Serendipity Model For News Recommendation

Jenders, Maximilian; Lindhauer, Thorben; Kasneci, Gjergji; Krestel, Ralf; Naumann, Felix in KI 2015: Advances in Artificial Intelligence - 38th Annual German Conference on AI, Dresden, Germany, September 21-25, 2015, Proceedings volume   9324   of   Lecture Notes in Computer Science 9324 , page 111-123 . Springer , 2015 .

Recommendation algorithms typically work by suggesting items that are similar to the ones that a user likes, or items that similar users like. We propose a content-based recommendation technique with the focus on serendipity of news recommendations. Serendipitous recommendations have the characteristic of being unexpected yet fortunate and interesting to the user, and thus might yield higher user satisfaction. In our work, we explore the concept of serendipity in the area of news articles and propose a general framework that incorporates the benefits of serendipity- and similarity-based recommendation techniques. An evaluation against other baseline recommendation models is carried out in a user study.
Further Information
Tags isg news_analysis web_science