Doerfel, S. & Jäschke, R.: An analysis of tag-recommender evaluation procedures. Proceedings of the 7th ACM conference on Recommender systems. New York, NY, USA: ACM, 2013RecSys '13 , S. 343-346
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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.

Landia, N.; Doerfel, S.; Jäschke, R.; Anand, S. S.; Hotho, A. & Griffiths, N.: Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations. In: cs.IR 1310.1498 (2013),
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The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.

Balby Marinho, L.; Hotho, A.; Jäschke, R.; Nanopoulos, A.; Rendle, S.; Schmidt-Thieme, L.; Stumme, G. & Symeonidis, P.: Recommender Systems for Social Tagging Systems. Springer, 2012SpringerBriefs in Electrical and Computer Engineering
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Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.

Doerfel, S.; Jäschke, R.; Hotho, A. & Stumme, G.: Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation . Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web. New York, NY, USA: ACM, 2012, S. 9-16
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The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.

Jäschke, R.; Hotho, A.; Mitzlaff, F. & Stumme, G.: Challenges in Tag Recommendations for Collaborative Tagging Systems. In: Pazos Arias, J. J.; Fernández Vilas, A. & Díaz Redondo, R. P. (Hrsg.): Recommender Systems for the Social Web. Berlin/Heidelberg: Springer, 2012 (Intelligent Systems Reference Library 32), S. 65-87
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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.

Schlitter, N. & Falkowski, T.: Mining the Dynamics of Music Preferences from a Social Networking Site. Proceedings of the International Conference on Advances in Social Network Analysis and Mining. 2009

Hotho, A.: Social Bookmarking. München, 2008
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Jäschke, R.; Marinho, L.; Hotho, A.; Schmidt-Thieme, L. & Stumme, G.: Tag Recommendations in Social Bookmarking Systems. In: AI Communications 21 (2008), Nr. 4, S. 231-247
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Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.
this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of
er-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.

Krause, B.; Hotho, A. & Stumme, G.: A Comparison of Social Bookmarking with Traditional Search. In: Macdonald, C.; Ounis, I.; Plachouras, V.; Ruthven, I. & White, R. W. (Hrsg.): Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008. Heidelberg: Springer, 2008 (LNAI 4956), S. 101-113

Social bookmarking systems allow users to store links to internet resources on a web page. As social bookmarking systems are growing in popularity, search algorithms have been developed that transfer the idea of link-based rankings in the Web to a social bookmarking system’s
ta structure. These rankings differ from traditional search engine rankings in that they incorporate the rating of users.

n this study, we compare search in social bookmarking systems with traditionalWeb search. In the first part, we compare the user activity and behaviour in both kinds of systems, as well as the overlap of the underlying sets of URLs. In the second part,we compare graph-based and vector space rankings for social bookmarking systems with commercial search engine rankings.

ur experiments are performed on data of the social bookmarking system Del.icio.us and on rankings and log data from Google, MSN, and AOL. We will show that part of the difference between the systems is due to different behaviour (e. g., the concatenation of multi-word lexems
single terms in Del.icio.us), and that real-world events may trigger similar behaviour in both kinds of systems. We will also show that a graph-based ranking approach on folksonomies yields results that are closer to the rankings of the commercial search engines than vector space
trieval, and that the correlation is high in particular for the domains that are well covered by the social bookmarking system.

Krause, B.; Schmitz, C.; Hotho, A. & Stumme, G.: The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems. AIRWeb '08: Proceedings of the 4th International Workshop on Adversarial Information Retrieval on the Web. New York, NY, USA: ACM, 2008, S. 61-68
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The annotation of web sites in social bookmarking systemshas become a popular way to manage and find informationon the web. The community structure of such systems attractsspammers: recent post pages, popular pages or specifictag pages can be manipulated easily. As a result, searchingor tracking recent posts does not deliver quality resultsannotated in the community, but rather unsolicited, oftencommercial, web sites. To retain the benefits of sharingone’s web content, spam-fighting mechanisms that can facethe flexible strategies of spammers need to be developed.

Krause, B.; Schmitz, C.; Hotho, A. & Stumme, G.: The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems. Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the Web. 2008
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Grahl, M.; Hotho, A. & Stumme, G.: Conceptual Clustering of Social Bookmark Sites. In: Hinneburg, A. (Hrsg.): Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007). Martin-Luther-Universität Halle-Wittenberg, 2007, S. 50-54
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Jaeschke, R.; Marinho, L.; Hotho, A.; Schmidt-Thieme, L. & Stumme, G.: Tag Recommendations in Folksonomies. In: Hinneburg, A. (Hrsg.): Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007). Martin-Luther-Universität Halle-Wittenberg, 2007, S. 13-20
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Jäschke, R.; Hotho, A.; Schmitz, C. & Stumme, G.: Analysis of the Publication Sharing Behaviour in BibSonomy. In: Priss, U.; Polovina, S. & Hill, R. (Hrsg.): Proceedings of the 15th International Conference on Conceptual Structures (ICCS 2007). Berlin, Heidelberg: Springer-Verlag, 2007 (Lecture Notes in Artificial Intelligence 4604), S. 283-295

BibSonomy is a web-based social resource sharing system which allows users to organise and share bookmarks and publications in a collaborative manner. In this paper we present the system, followed by a description of the insights in the structure of its bibliographic data that we gained by applying techniques we developed in the area of Formal Concept Analysis.

Koutrika, G.; Effendi, F. A.; Gyöngyi, Z.; Heymann, P. & Garcia-Molina, H.: Combating spam in tagging systems. AIRWeb '07: Proc. of the 3rd int. workshop on Adversarial inf. retrieval on the web. 2007, S. 57-64

Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.: BibSonomy: A Social Bookmark and Publication Sharing System. Proc. of the ICCS 2006 Conceptual Structures Tool Interoperability
Workshop. 2006

Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.: BibSonomy: A Social Bookmark and Publication Sharing System. In: de Moor, A.; Polovina, S. & Delugach, H. (Hrsg.): Proceedings of the First Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures. Aalborg: Aalborg Universitetsforlag, 2006, S. 87-102
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Social bookmark tools are rapidly emerging on the Web. In such
stems users are setting up lightweight conceptual structures
lled folksonomies. The reason for their immediate success is the
ct that no specific skills are needed for participating. In this
per we specify a formal model for folksonomies and briefly describe
r own system BibSonomy, which allows for sharing both bookmarks
d publication references in a kind of personal library.

Millen, D. R.; Feinberg, J. & Kerr, B.: Dogear: Social bookmarking in the enterprise. CHI '06: Proceedings of the SIGCHI conference on Human Factors in computing systems. New York, NY, USA: ACM Press, 2006, S. 111-120

Hammond, T.; Hannay, T.; Lund, B. & Scott, J.: Social Bookmarking Tools (I): A General Review. In: D-Lib Magazine 11 (2005), Nr. 4,

Lund, B.; Hammond, T.; Flack, M. & Hannay, T.: Social Bookmarking Tools (II): A Case Study - Connotea. In: D-Lib Magazine 11 (2005), Nr. 4,