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    DMIR Research Group

    Recommender Systems

    The DMIR group 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 DMIR Group.

    Projects

    We are currently working on the following projects:

    adidas

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

    BibSonomy

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

    Publications

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

    • 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 for S... - Download
      Tag Recommendations for SensorFolkSonomies.Mueller, Juergen; Doerfel, Stephan; Becker, Martin; Hotho, Andreas; Stumme, Gerd (2013). (Vol. 1066)
       
    • Leveraging publication me... - Download
      Leveraging publication metadata and social data into FolkRank for scientific publication recommendation.Doerfel, Stephan; Jäschke, Robert; Hotho, Andreas; Stumme, Gerd in RSWeb '12 (2012). 9--16.
       
    • 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.
       

    Challenges

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

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    Data privacy protection

    By clicking 'OK' you are leaving the web sites of the Julius-Maximilians-Universität Würzburg and will be redirected to Twitter. For information on the collection and processing of data by Facebook, refer to the social network's data privacy statement.

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    Contact

    Andreas Hotho
    DMIR Research Group
    Am Hubland
    97074 Würzburg

    Phone: +49 931 31-86731
    Fax: +49 931 31-86732

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