Jäschke, R.; Hotho, A.; Mitzlaff, F. & Stumme, G. (2012), Challenges in Tag Recommendations for Collaborative Tagging Systems, in José J. Pazos Arias; Ana Fernández Vilas & Rebeca P. Díaz Redondo, ed., 'Recommender Systems for the Social Web' , Springer, Berlin/Heidelberg , pp. 65--87 .
[BibTeX] [Endnote]

Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.

Jäschke, R.; Hotho, A.; Mitzlaff, F. & Stumme, G. (2012), Challenges in Tag Recommendations for Collaborative Tagging Systems, in Janusz Kacprzyk & Lakhmi C. Jain, ed., 'Recommender Systems for the Social Web' , Springer, Berlin/Heidelberg , pp. 65--87 .
[BibTeX] [Endnote]

Landia, N.; Anand, S. S.; Hotho, A.; Jäschke, R.; Doerfel, S. & Mitzlaff, F. (2012), Extending FolkRank with content data, in 'Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web' , ACM, New York, NY, USA , pp. 1--8 .
[BibTeX] [Endnote]

Real-world tagging datasets have a large proportion of new/ untagged documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for new documents, approaches are required which model documents not only based on the tags assigned to them in the past (if any), but also the content. In this paper we present a novel adaptation to the widely recognised FolkRank tag recommendation algorithm by including content data. We adapt the FolkRank graph to use word nodes instead of document nodes, enabling it to recommend tags for new documents based on their textual content. Our adaptations make FolkRank applicable to post-core 1 ie. the full real-world tagging datasets and address the new item problem in tag recommendation. For comparison, we also apply and evaluate the same methodology of including content on a simpler tag recommendation algorithm. This results in a less expensive recommender which suggests a combination of user related and document content related tags.</p> <p>Including content data into FolkRank shows an improvement over plain FolkRank on full tagging datasets. However, we also observe that our simpler content-aware tag recommender outperforms FolkRank with content data. Our results suggest that an optimisation of the weighting method of FolkRank is required to achieve better results.

Landia, N.; Anand, S. S.; Hotho, A.; Jäschke, R.; Doerfel, S. & Mitzlaff, F. (2012), Extending FolkRank with Content Data, in 'Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web' , ACM, New York, NY, USA , pp. 1--8 .
[BibTeX] [Endnote]

Real-world tagging datasets have a large proportion of new/ untagged documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for new documents, approaches are required which model documents not only based on the tags assigned to them in the past (if any), but also the content. In this paper we present a novel adaptation to the widely recognised FolkRank tag recommendation algorithm by including content data. We adapt the FolkRank graph to use word nodes instead of document nodes, enabling it to recommend tags for new documents based on their textual content. Our adaptations make FolkRank applicable to post-core 1 ie. the full real-world tagging datasets and address the new item problem in tag recommendation. For comparison, we also apply and evaluate the same methodology of including content on a simpler tag recommendation algorithm. This results in a less expensive recommender which suggests a combination of user related and document content related tags. Including content data into FolkRank shows an improvement over plain FolkRank on full tagging datasets. However, we also observe that our simpler content-aware tag recommender outperforms FolkRank with content data. Our results suggest that an optimisation of the weighting method of FolkRank is required to achieve better results.

2011

Mitzlaff, F.; Atzmueller, M.; Benz, D.; Hotho, A. & Stumme, G. (2011), Community Assessment Using Evidence Networks, in Martin Atzmueller; Andreas Hotho; Markus Strohmaier & Alvin Chin, ed., 'Analysis of Social Media and Ubiquitous Data' , Springer Berlin Heidelberg, , pp. 79-98 .
[BibTeX] [Endnote]

Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups.

2010

Benz, D.; Hotho, A.; Jäschke, R.; Krause, B.; Mitzlaff, F.; Schmitz, C. & Stumme, G. (2010), 'The Social Bookmark and Publication Management System BibSonomy', The VLDB Journal 19 (6) , 849--875 .
[BibTeX] [Endnote]

Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.

Mitzlaff, F.; Benz, D.; Stumme, G. & Hotho, A. (2010), Visit me, click me, be my friend: an analysis of evidence networks of user relationships in BibSonomy, in 'HT '10: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia' , ACM, New York, NY, USA , pp. 265--270 .
[BibTeX] [Endnote]

The ongoing spread of online social networking and sharing sites has reshaped the way how people interact with each other. Analyzing the relatedness of different users within the resulting large populations of these systems plays an important role for tasks like user recommendation or community detection. Algorithms in these fields typically face the problem that explicit user relationships (like friend lists) are often very sparse. Surprisingly, implicit evidences (like click logs) of user relations have hardly been considered to this end. Based on our long-time experience with running BibSonomy [4], we identify in this paper different evidence networks of user relationships in our system. We broadly classify each network based on whether the links are explicitly established by the users (e.g., friendship or group membership) or accrue implicitly in the running system (e.g., when user u copies an entry of user v). We systematically analyze structural properties of these networks and whether topological closeness (in terms of the length of shortest paths) coincides with semantic similarity between users.