Data Science Chair

    Recommender Systems

    The Data Science Chair develops new methods to recommend items to users to enhance their overall user experience in a variety of application scenarios. For example, we help users to find relevant products to buy or users of our social bookmarking system BibSonomy to annotate websites and publication with keywords (tags). In this context we leverage various information like navigation paths, user behavior or buying behavior. We utilize different machine learning algorithms, including deep learning, for our recommendation methods. To validate some of our methods, we can deploy and test our methods in our live system BibSonomy, which is run and developed by the Data Science Chair.


    We are currently working on the following projects:


    We are analysing the user behavior in the Adidas web shop to improve item recommendations.


    The social bookmarking system, that enables us to test our new recommendation methods.


    We recommend medical examinations for patients in the University Hospital Würzburg.


    Contributing to a better understanding of the role of geographic and thematic proximity in the success of scientific research.


    Here is a list of selected publications. You can find the full list here.

    • A Case Study on Sampling ... - Download
      A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models. Dallmann, Alexander; Zoller, Daniel; Hotho, Andreas (2021).
    • Comparison of Transformer... - Download
      Comparison of Transformer-Based Sequential Product Recommendation Models for the Coveo Data Challenge. Fischer, Elisabeth; Zoller, Daniel; Hotho, Andreas in SIGIR Workshop On eCommerce (2021).
    • Integrating Keywords into... - Download
      Integrating Keywords into BERT4Rec for Sequential Recommendation. Fischer, Elisabeth; Zoller, Daniel; Dallmann, Alexander; Hotho, Andreas (2020).
    • Improving Session Recomme... - Download
      Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell Time. Dallmann, Alexander; Grimm, Alexander; Pölitz, Christian; Zoller, Daniel; Hotho, Andreas in CoRR (2017). abs/1706.10231
    • Leveraging User-Interacti... - Download
      Leveraging User-Interactions for Time-Aware Tag Recommendations. Zoller, Daniel; Doerfel, Stephan; Pölitz, Christian; Hotho, Andreas in CEUR Workshop Proceedings (2017).
    • Tag Recommendations in So... - Download
      Tag Recommendations in Social Bookmarking Systems. Jäschke, Robert; Marinho, Leandro; Hotho, Andreas; Schmidt-Thieme, Lars; Stumme, Gerd in AI Communications, (E. Giunchiglia, ed.) (2008). 21(4) 231–247.


    We also co-organized recommendation challenges to allow other researchers to develop new recommendation methods: