Data Science Chair

    Machine Learning for Recommender Systems

    Our work in the field of recommendation is focused on researching and developing recommender systems for a diverse range of application fields in e-commerce, chat interactions, clinical diagnostics and publication data. We are interested in sequential recommendation, but have also worked on content-based tag recommendation in the past. 

    We have cooperated with industrial partners such as adidas to develop personalized product recommendations based on sequences of user interactions. Here, we focus on using additional content to improve recommendations, for example we show how recent transformer models can profit from categorical item information in KeBERT4Rec and propose personalized recommendations based on user attributes in our paper "Personalization through User Attributes for Transformer-based Sequential Recommendation ( Link).

    We have also used sequential transformer models for item recommendation in the MOBA game Dota ( Link ) and to predict scientific collaborations in our paper “CoBERT: Scientific Collaboration Prediction via Sequential Recommendation” (Link).

    Together with the University Hospital Würzburg we aim to predict patients' next diagnostics requests in the DZ.PTM project. In our recent position paper “Towards Responsible Medical Diagnostics Recommendation Systems ( Link )'' we outline the criteria for working on recommender systems in the medical context while securing fairness, accountability, compliance and safety aspects.

    On a more general note, we’ve published “A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models ( Link )” where we examine different sampling strategies for the calculation of metrics and show the influence on the ranking and evaluation of recommendation models.

    In KiLiMod, we aim to provide personalized, relevant content recommendations for participants of live-stream chats as available on Twitch for e-sport events. 

    We have also researched tag recommendation for folksonomies with the help of our social bookmark system BibSonomy  and published a number of works. For example, in “Comparison of Content-Based Tag Recommendations in Folksonomy Systems" (  Link) we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset. We also have tested and evaluated tag recommendations live in BibSonomy and presented the results in  “Testing and Evaluating Tag Recommenders in a Live System" ( Link).

    You can find a list of selected publications below.




    Machine learning based chat moderation and content enrichment


    Developing methods for Hypothesis-driven Analysis of Sequential Data. 


    The social bookmarking system, that enables users to share and annotate bookmarks and publications.

    Concluded Projects

    • adidas - Modeling and Personalization for Customer Engagement

    Selected Publications

    For a complete list of our papers to this topic, see  here.

    • Personalization through U...
      Personalization through User Attributes for Transformer-based Sequential Recommendation. Fischer, Elisabeth; Dallmann, Alexander; Hotho, Andreas. 2022.
    • Towards Responsible Medical Diagnostics Recommendation Systems. Schlör, Daniel; Hotho, Andreas. 2022.
    • Sequential Item Recommendation in the MOBA Game Dota 2. Dallmann, Alexander; Kohlmann, Johannes; Zoller, Daniel; Hotho, Andreas. In CoRR, abs/2201.08724. 2022.
    • CoBERT: Scientific Collab...
      CoBERT: Scientific Collaboration Prediction via Sequential Recommendation. Koopmann, Tobias; Kobs, Konstantin; Herud, Konstantin; Hotho, Andreas. In 2021 International Conference on Data Mining Workshops (ICDMW), pp. 45–54. 2021.
    • A Case Study on Sampling ...
      A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models. Dallmann, Alexander; Zoller, Daniel; Hotho, Andreas. In Fifteenth ACM Conference on Recommender Systems. ACM, 2021.
    • Leveraging User-Interacti...
      Leveraging User-Interactions for Time-Aware Tag Recommendations. Zoller, Daniel; Doerfel, Stephan; Pölitz, Christian; Hotho, Andreas. In Proceedings of the Workshop on Temporal Reasoning in Recommender Systems, of CEUR Workshop Proceedings. 2017.
    • Improving Session Recomme...
      Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell Time. Dallmann, Alexander; Grimm, Alexander; Pölitz, Christian; Zoller, Daniel; Hotho, Andreas. In CoRR, abs/1706.10231. 2017.