Bullock, B. N.; Jäschke, R. & Hotho, A. (2011), Tagging data as implicit feedback for learning-to-rank, in 'Proceedings of the ACM WebSci'11' .
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Burke, R.; Gemmell, J.; Hotho, A. & Jäschke, R. (2011), 'Recommendation in the Social Web', AI Magazine 32 (3) , 46--56 .
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Recommender systems are a means of personalizing the presentation of information to ensure that users see the items most relevant to them. The social web has added new dimensions to the way people interact on the Internet, placing the emphasis on user-generated content. Users in social networks create photos, videos and other artifacts, collaborate with other users, socialize with their friends and share their opinions online. This outpouring of material has brought increased attention to recommender systems, as a means of managing this vast universe of content. At the same time, the diversity and complexity of the data has meant new challenges for researchers in recommendation. This article describes the nature of recommendation research in social web applications and provides some illustrative examples of current research directions and techniques. It is difficult to overstate the impact of the social web. This new breed of social applications is reshaping nearly every human activity from the way people watch movies to how they overthrow governments. Facebook allows its members to maintain friendships whether they live next door or on another continent. With Twitter, users from celebrities to ordinary folks can launch their 140 character messages out to a diverse horde of ‘‘followers.” Flickr and YouTube users upload their personal media to share with the world, while Wikipedia editors collaborate on the world’s largest encyclopedia.

Illig, J.; Hotho, A.; Jäschke, R. & Stumme, G. (2011), A Comparison of Content-Based Tag Recommendations in Folksonomy Systems, in Karl Erich Wolff; Dmitry E. Palchunov; Nikolay G. Zagoruiko & Urs Andelfinger, ed., 'Knowledge Processing and Data Analysis' , Springer, Berlin/Heidelberg , pp. 136--149 .
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Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset.

Illig, J.; Hotho, A.; Jäschke, R. & Stumme, G. (2011), A Comparison of content-based Tag Recommendations in Folksonomy Systems, in 'Postproceedings of the International Conference on Knowledge Processing in Practice (KPP 2007)' , Springer, .
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2010

Benz, D.; Hotho, A.; Jäschke, R.; Krause, B. & Stumme, G. (2010), 'Query Logs as Folksonomies', Datenbank-Spektrum 10 (1) , 15--24 .
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Query logs provide a valuable resource for preference information in search. A user clicking on a specific resource after submitting a query indicates that the resource has some relevance with respect to the query. To leverage the information ofquery logs, one can relate submitted queries from specific users to their clicked resources and build a tripartite graph ofusers, resources and queries. This graph resembles the folksonomy structure of social bookmarking systems, where users addtags to resources. In this article, we summarize our work on building folksonomies from query log files. The focus is on threecomparative studies of the system’s content, structure and semantics. Our results show that query logs incorporate typicalfolksonomy properties and that approaches to leverage the inherent semantics of folksonomies can be applied to query logsas well.

2009

Benz, D.; Eisterlehner, F.; Hotho, A.; Jäschke, R.; Krause, B. & Stumme, G. (2009), Managing publications and bookmarks with BibSonomy, in Ciro Cattuto; Giancarlo Ruffo & Filippo Menczer, ed., 'HT '09: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia' , ACM, New York, NY, USA , pp. 323--324 .
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2008

Benz, D.; Grobelnik, M.; Hotho, A.; Jäschke, R.; Mladenic, D.; Servedio, V. D. P.; Sizov, S. & Szomszor, M. (2008), Analyzing Tag Semantics Across Collaborative Tagging Systems, in Harith Alani; Steffen Staab & Gerd Stumme, ed., 'Proceedings of the Dagstuhl Seminar on Social Web Communities' .
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The objective of our group was to exploit state-of-the-art Information Retrieval methods for finding associations and dependencies between tags, capturing and representing differences in tagging behavior and vocabulary of various folksonomies, with the overall aim to better understand the semantics of tags and the tagging process. Therefore we analyze the semantic content of tags in the Flickr and Delicious folksonomies. We find that: tag context similarity leads to meaningful results in Flickr, despite its narrow folksonomy character; the comparison of tags across Flickr and Delicious shows little semantic overlap, being tags in Flickr associated more to visual aspects rather than technological as it seems to be in Delicious; there are regions in the tag-tag space, provided with the cosine similarity metric, that are characterized by high density; the order of tags inside a post has a semantic relevance.

Jäschke, R.; Hotho, A.; Schmitz, C.; Ganter, B. & Stumme, G. (2008), 'Discovering Shared Conceptualizations in Folksonomies', Journal of Web Semantics 6 (1) , 38-53 .
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Jäschke, R.; Krause, B.; Hotho, A. & Stumme, G. (2008), Logsonomy -- A Search Engine Folksonomy, in 'Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)' , AAAI Press, .
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In social bookmarking systems users describe bookmarksby keywords called tags. The structure behindthese social systems, called folksonomies, can beviewed as a tripartite hypergraph of user, tag and resourcenodes. This underlying network shows specificstructural properties that explain its growth and the possibilityof serendipitous exploration.Search engines filter the vast information of the web.Queries describe a user’s information need. In responseto the displayed results of the search engine, users clickon the links of the result page as they expect the answerto be of relevance. The clickdata can be represented as afolksonomy in which queries are descriptions of clickedURLs. This poster analyzes the topological characteristicsof the resulting tripartite hypergraph of queries,users and bookmarks of two query logs and compares ittwo a snapshot of the folksonomy del.icio.us.

Jäschke, R.; Marinho, L.; Hotho, A.; Schmidt-Thieme, L. & Stumme, G. (2008), 'Tag Recommendations in Social Bookmarking Systems', AI Communications 21 (4) , 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. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-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.; Jäschke, R.; Hotho, A. & Stumme, G. (2008), Logsonomy - Social Information Retrieval with Logdata, in 'HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia' , ACM, New York, NY, USA , pp. 157--166 .
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Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today’s search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.

2007

Jäschke, R.; Marinho, L. B.; Hotho, A.; Schmidt-Thieme, L. & Stumme, G. (2007), Tag Recommendations in Folksonomies, in Joost N. Kok; Jacek Koronacki; Ramon López de Mántaras; Stan Matwin; Dunja Mladenic & Andrzej Skowron, ed., 'Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases' , Springer, Berlin, Heidelberg , pp. 506-514 .
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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. In this paper we evaluate and compare two recommendation algorithms on largescale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.

2006

Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G. (2006), FolkRank: A Ranking Algorithm for Folksonomies, in 'Proc. FGIR 2006' .
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In social bookmark tools users are setting up lightweight conceptual structures called folksonomies. Currently, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. A long version of this paper has been published at the European Semantic Web Conference 2006.

Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G. (2006), Trend Detection in Folksonomies, in Yannis S. Avrithis; Yiannis Kompatsiaris; Steffen Staab & Noel E. O'Connor, ed., 'Proc. First International Conference on Semantics And Digital Media Technology (SAMT) ' , Springer, Heidelberg , pp. 56-70 .
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As the number of resources on the web exceeds by far the number ofdocuments one can track, it becomes increasingly difficult to remainup to date on ones own areas of interest. The problem becomes moresevere with the increasing fraction of multimedia data, from whichit is difficult to extract some conceptual description of theircontents.One way to overcome this problem are social bookmark tools, whichare rapidly emerging on the web. In such systems, users are settingup lightweight conceptual structures called folksonomies, andovercome thus the knowledge acquisition bottleneck. As more and morepeople participate in the effort, the use of a common vocabularybecomes more and more stable. We present an approach for discoveringtopic-specific trends within folksonomies. It is based on adifferential adaptation of the PageRank algorithm to the triadichypergraph structure of a folksonomy. The approach allows for anykind of data, as it does not rely on the internal structure of thedocuments. In particular, this allows to consider different datatypes in the same analysis step. We run experiments on a large-scalereal-world snapshot of a social bookmarking system.

Jäschke, R.; Hotho, A.; Schmitz, C.; Ganter, B. & Stumme, G. (2006), TRIAS - An Algorithm for Mining Iceberg Tri-Lattices, in 'Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 06)' , IEEE Computer Society, Hong Kong , pp. 907-911 .
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Jäschke, R.; Hotho, A.; Schmitz, C. & Stumme, G. (2006), Wege zur Entdeckung von Communities in Folksonomies, in Stefan Braß & Alexander Hinneburg, ed., 'Proc. 18. Workshop Grundlagen von Datenbanken' , Martin-Luther-Universität , Halle-Wittenberg , pp. 80-84 .
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Ein wichtiger Baustein des neu entdeckten World Wide Web -- des "`Web 2.0"' -- stellen Folksonomies dar. In diesen Systemen können Benutzer gemeinsam Ressourcen verwalten und mit Schlagwörtern versehen. Die dadurch entstehenden begrifflichen Strukturen stellen ein interessantes Forschungsfeld dar. Dieser Artikel untersucht Ansätze und Wege zur Entdeckung und Strukturierung von Nutzergruppen ("Communities") in Folksonomies.

Schmitz, C.; Hotho, A.; Jäschke, R. & Stumme, G. (2006), Content Aggregation on Knowledge Bases using Graph Clustering, in York Sure & John Domingue, ed., 'The Semantic Web: Research and Applications' , Springer, Heidelberg , pp. 530-544 .
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Recently, research projects such as PADLR and SWAP have developed tools like Edutella or Bibster, which are targeted at establishing peer-to-peer knowledge management (P2PKM) systems. In such a system, it is necessary to obtain provide brief semantic descriptions of peers, so that routing algorithms or matchmaking processes can make decisions about which communities peers should belong to, or to which peers a given query should be forwarded. This paper provides a graph clustering technique on knowledge bases for that purpose. Using this clustering, we can show that our strategy requires up to 58% fewer queries than the baselines to yield full recall in a bibliographic P2PKM scenario.

Schmitz, C.; Hotho, A.; Jäschke, R. & Stumme, G. (2006), Mining Association Rules in Folksonomies, in V. Batagelj; H.-H. Bock; A. Ferligoj & A. vZiberna, ed., 'Data Science and Classification: Proc. of the 10th IFCS Conf.' , Springer, Berlin, Heidelberg , pp. 261--270 .
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