Prof. Dr. Felix Naumann

Julian Risch

I am a Ph.D. student at the Information Systems Group and a member of the HPI Research School. My research focuses on topic modeling and deep learning with applications in the field of text mining, in particular, comment analysis. Further, I am involved in projects on patent classification and book recommendation.

Source code for my publications can be found here and on GitHub.

Contact Information

Prof.-Dr.-Helmert-Straße 2-3
D-14482 Potsdam
Room: F-2.08

Phone: +49 331 5509 272

Email: Julian Risch

Open Master's Theses

I provide supervision for Master's theses in the area of News Comment Analysis, e.g., Toxic Comment Classification, User Engagement Prediction, Comment Recommendation, and Discussion Summarization/Visualization. Feel free to schedule an informal meeting with me to discuss details of these topics and/or your own ideas.


Advised Master's Theses

  • Enriching Document Embeddings With Domain Knowledge
  • Modeling News Commenters for Discussion Recommendation
  • Jointly Learning Document and Label Embeddings for Hierarchically Labeled Text
  • Context-aware Classification of News Comments
  • Quality Management for Online News Comments 


What Should I Cite? Cross-Collection Reference Recommendation of Patents and Papers

Risch, Julian; Krestel, Ralf in Proceedings of the International Conference on Theory and Practice of Digital Libraries (TPDL) Seite 40-46 . 2017 .

Research results manifest in large corpora of patents and scientific papers. However, both corpora lack a consistent taxonomy and references across different document types are sparse. Therefore, and because of contrastive, domain-specific language, recommending similar papers for a given patent (or vice versa) is challenging. We propose a hybrid recommender system that leverages topic distributions and key terms to recommend related work despite these challenges. As a case study, we evaluate our approach on patents and papers of two fields: medical and computer science. We find that topic-based recommenders complement term-based recommenders for documents with collection-specific language and increase mean average precision by up to 23%. As a result of our work, publications from both corpora form a joint digital library, which connects academia and industry.
Weitere Informationen
Tagsisg  topic_models  web_science