- Informatik in Kassel
- FG Wissensverarbeitung
- Technikgestaltung @ ITeG
- Web Science @ L3S
- Source Code
- Data Sets
Dipl.-Math. Stephan Doerfel
- Universität Kassel, Fachgebiet Wissensverarbeitung
- Wilhelmshöher Allee 73
- 34121 Kassel (Germany)
- Raum 0445F
- Telefon: +49 (0)561 804-6252
- Fax: +49 (0)561 804-6259
My research at Kassel University includes the investigation of
scientometrics and webometrics, correlations thereof and their possible applications for recommendations and rankings.
Further interests include formal concept analysis, social network analysis, and machine learning in general.
I am also a senior developer of the blue social bookmark and publication sharing system BibSonomy.
Full list of my publications and posters.
- Doerfel, S., Jäschke, R., Stumme, G.: The Role of Cores in Recommender Benchmarking for Social Bookmarking Systems. Transactions on Intelligent Systems and Technology. (in press), (2016).Social bookmarking systems have established themselves as an important part in today’s web. In such systems, tag recommender systems support users during the posting of a resource by suggesting suitable tags. Tag recommender algorithms have often been evaluated in offline benchmarking experiments. Yet, the particular setup of such experiments has rarely been analyzed. In particular, since the recommendation quality usually suffers from difficulties like the sparsity of the data or the cold start problem for new resources or users, datasets have often been pruned to so-called cores (specific subsets of the original datasets) – however without much consideration of the implications on the benchmarking results. In this paper, we generalize the notion of a core by introducing the new notion of a set-core – which is independent of any graph structure – to overcome a structural drawback in the previous constructions of cores on tagging data. We show that problems caused by some types of cores can be eliminated using setcores. Further, we present a thorough analysis of tag recommender benchmarking setups using cores. To that end, we conduct a large-scale experiment on four real-world datasets in which we analyze the influence of different cores on the evaluation of recommendation algorithms. We can show that the results of the comparison of different recommendation approaches depends on the selection of core type and level. For the benchmarking of tag recommender algorithms, our results suggest that the evaluation must be set up more carefully and should not be based on one arbitrarily chosen core type and level.Zoller, D., Doerfel, S., Jäschke, R., Stumme, G., Hotho, A.: On Publication Usage in a Social Bookmarking System. Proceedings of the 2015 ACM Conference on Web Science (2015).Scholarly success is traditionally measured in terms of citations to publications. With the advent of publication man- agement and digital libraries on the web, scholarly usage data has become a target of investigation and new impact metrics computed on such usage data have been proposed – so called altmetrics. In scholarly social bookmarking sys- tems, scientists collect and manage publication meta data and thus reveal their interest in these publications. In this work, we investigate connections between usage metrics and citations, and find posts, exports, and page views of publications to be correlated to citations.Doerfel, S., Zoller, D., Singer, P., Niebler, T., Hotho, A., Strohmaier, M.: How Social is Social Tagging? Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion. pp. 251-252. International World Wide Web Conferences Steering Committee, Seoul, Korea (2014).Social tagging systems have established themselves as an important part in today's web and have attracted the interest of our research community in a variety of investigations. This has led to several assumptions about tagging, such as that tagging systems exhibit a social component. In this work we overcome the previous absence of data for testing such an assumption. We thoroughly study social interaction, leveraging for the first time live log data gathered from the real-world public social tagging system \bibs. Our results indicate that sharing of resources constitutes an important and indeed social aspect of tagging.Doerfel, S., Jäschke, R.: An Analysis of Tag-Recommender Evaluation Procedures. Proceedings of the 7th ACM conference on Recommender systems (RecSys 2013). pp. 343-346. ACM, Hong Kong, China (2013).Since the rise of collaborative tagging systems on the web, the tag recommendation task -- suggesting suitable tags to users of such systems while they add resources to their collection -- has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.Doerfel, S., Hotho, A., Kartal-Aydemir, A., Roßnagel, A., Stumme, G.: Informationelle Selbstbestimmung Im Web 2.0 - Chancen Und Risiken Sozialer Verschlagwortungssysteme. Vieweg + Teubner Verlag (2013).Die neue Generation des Internets („Web 2.0“ oder „Social Web“) zeichnet sich durch eine sehr freizügige Informationsbereitstellung durch seine Nutzer aus. Vor diesem Hintergrund haben Informatiker und Juristen in enger Interaktion die Chancen und Risiken der neuen Web 2.0-Technologien erkundet und gestaltet. Nach Bestandsaufnahme werden die technischen und rechtlichen Chancen und Risiken bezogen auf typisierte Aufgaben analysiert. Generische Konzepte für die datenschutzgerechte Gestaltung einer Anwendung wie Identitätsmanagement, Vermeidung von Personenbezug, Profilbildung und Verantwortlichkeiten werden erarbeitet. Parallel dazu werden Algorithmen und Verfahren für diese Konzepte vorgestellt: Recommender-Systeme für kooperative Verschlagwortungssysteme sowie Spam-Entdeckungsverfahren für solche Systeme. Sie werden anhand realer Daten evaluiert. Alle Ergebnisse werden anhand des Social Bookmarking-Systems BibSonomy erläutert. Schließlich wird diskutiert, inwieweit Dogmatik und Auslegung des Datenschutzrechts wegen der neuen Problemlagen des Web 2.0 verändert werden müssen und eventuell gesetzgeberische Aktivitäten erforderlich oder ratsam sind.
projectsThese are the projects I have mainly worked on. Through my research, I have further been involved with the projects Venus, EveryAware, and PoSTs.
PUMAIn the DFG funded project "Akademisches Publikationsmanagement" (PUMA), we used and extended the BibSonomy software to create a new web portal that is run locally at an institution (library, university, etc.) and that integrates there with the existing eco system by connecting to the local open access repository, the publication discovery service or the eLearning platform.
Info 2.0In the DFG funded Project "Informationelle Selbstbestimmung im Web 2.0" (Info 2.0), we analyzed opportunities and risks in Web 2.0 systems, regarding issues of privacy. A second aspect that was investigated were consequences of user-generated ratings and reviews of products and particularly of scholarly publications. The project's results have been published as a book.
BibSonomyBibSonomy is a scholarly social bookmarking system where researchers manage their collections of publications and web pages. BibSonomy is an open source project, continously developed by researchers in Kassel, Würzburg, and Hannover. Functioning as a test bed for recommendation and ranking algorithms, as well as through the publicly available datasets, containing traces of user behavior on the Web, BibSonomy has been the subject of various scientific studies.
Full list of my reviewing and teaching activities.
- PC Member: 2nd International Workshop on Machine learning, Optimization and Big Data (MOD 2016), August 26 - 29, 2016, Volterra, Italy.
- PC Member: 13th International Conference on Concept Lattices and their Applications (CLA 2016), July 11 - 15, 2016, Moscow, Russia.
- PC Member: 7th International Workshop on Modeling Social Media (MSM 2016) - Behavioral Analytics in Social Media, Big Data and the Web, May 12th, 2016, Montreal, Canada.
- Journal Reviewer: IEEE Transactions on Services Computing (2015)
- PC Member: Workshop "Knowledge Discovery, Data Mining, Maschinelles Lernen 2015" der Fachgruppe KDML (KDML 2015), October 7 - 9, 2015, Trier, Germany.
- PC Member: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2015), September 7 - 11, 2015, Porto, Portugal.
- PC Member: 13th International Conference on Formal Concept Analysis (ICFCA 2015), June 23 - 26, 2015, Nerja (Málaga), Spain.
- Subreviewer: 14th International Semantic Web Conference (ISWC 2015), October 11 - 15, 2015, Bethlehem, Pennsylvania.
- Sommersemester 2015