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 our 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 


Book Recommendation Beyond the Usual Suspects: Embedding Book Plots Together with Place and Time Information

Risch, Julian; Garda, Samuele; Krestel, Ralf in Proceedings of the 20th International Conference On Asia-Pacific Digital Libraries (ICADL) Seite 227-239 . 2018 .

Content-based recommendation of books and other media is usually based on semantic similarity measures. While metadata can be compared easily, measuring the semantic similarity of narrative literature is challenging. Keyword-based approaches are biased to retrieve books of the same series or do not retrieve any results at all in sparser libraries. We propose to represent plots with dense vectors to foster semantic search for similar plots even if they do not have any words in common. Further, we propose to embed plots, places, and times in the same embedding space. Thereby, we allow arithmetics on these aspects. For example, a book with a similar plot but set in a different, user-specified place can be retrieved. We evaluate our findings on a set of 16,000 book synopses that spans literature from 500 years and 200 genres and compare our approach to a keyword-based baseline.
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Tagshpi  myown  web_science