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:
Here is a list of selected publications. You can find the full list here.
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 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.
Integrating Keywords into BERT4Rec for Sequential Recommendation. Fischer, Elisabeth; Zoller, Daniel; Dallmann, Alexander; Hotho, Andreas (2020).
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.
Leveraging User-Interactions for Time-Aware Tag Recommendations. Zoller, Daniel; Doerfel, Stephan; Pölitz, Christian; Hotho, Andreas in CEUR Workshop Proceedings (2017).
We also co-organized recommendation challenges to allow other researchers to develop new recommendation methods: