BibSonomy publications for /user/stumme/myownMon Feb 06 14:59:32 CET 2012Berlin/HeidelbergRecommender Systems for the Social Web65--87Intelligent Systems Reference LibraryChallenges in Tag Recommendations for Collaborative Tagging Systems3220122012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging 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.Tue Jan 17 16:41:44 CET 2012Heraklion, CreteProceedings of the 8th Extended Semantic Web Conference (ESWC 2011)mayOne Tag to Bind Them All: Measuring Term Abstractness in Social Metadata20112011 abstractness bind itegpub measuring myown social term Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.Wed Jan 04 11:35:08 CET 2012Analysis of Social Media and Ubiquitous DataLNAI{Community Assessment using Evidence Networks}690420112011 community itegpub knowledge mining myown Wed Jan 04 11:34:35 CET 2012Working Notes of the LWA 2011 - Learning, Knowledge, AdaptationOne Tag to Bind Them All: Measuring Term Abstractness in Social Metadata20112011 abstractness itegpub measuring metadata myown social Wed Jan 04 11:32:55 CET 2012New York, NY, USAProceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies15:1--15:8i-KNOW '11Privacy-aware spam detection in social bookmarking systems20112011 aware classification data-mining detection itegpub myown social spam spam-detection web2.0 web20 With the increased popularity of Web 2.0 services in the last years data privacy has become a major concern for users. The more personal data users reveal, the more difficult it becomes to control its disclosure in the web. However, for Web 2.0 service providers, the data provided by users is a valuable source for offering effective, personalised data mining services. One major application is the detection of spam in social bookmarking systems: in order to prevent a decrease of content quality, providers need to distinguish spammers and exclude them from the system. They thereby experience a conflict of interests: on the one hand, they need to identify spammers based on the information they collect about users, on the other hand, they need to respect privacy concerns and process as few personal data as possible. It would therefore be of tremendous help for system developers and users to know which personal data are needed for spam detection and which can be ignored. In this paper we address these questions by presenting a data privacy aware feature engineering approach. It consists of the design of features for spam classification which are evaluated according to both, performance and privacy conditions. Experiments using data from the social bookmarking system BibSonomy show that both conditions must not exclude each other.Wed Jan 04 11:32:27 CET 2012Informatik 2011 - Informatik schafft Communities - Proceedings der 41. GI-Jahrestagung 10412Lecture Notes in InformaticsPrivatsphären- und Datenschutz in Community-Plattformen: Gestaltung von Online-Bewertungsportalen19220112011 community gi info20 itegpub myown privat Aufgrund der mittlerweile unüberschaubaren Vielfalt von Anwendungsmöglichkeiten des Web 2.0, findet man fast zu jedem Lebensbereich eine passende Community im Netz. Dabei steigt auch die Anzahl der Bewertungsportale stetig und betrifft längst nicht mehr nur die Bewertung von Waren, sondern erstreckt sich unterdessen auch auf Beurteilungen von Leistungen und Eigenschaften von zu bestimmten Berufsgruppen gehörenden Personen. Diese Entwicklung birgt die Gefahr, dass die dadurch gewonnenen persönlichen Daten durchaus geeignet sind, wahrheitswidrig ein übermäßig positives oder übermäßig negatives Persönlichkeitsbild des Betroffenen zu konstruieren und dadurch sein Ansehen zu beeinflussen. Im Hinblick auf Fragen im Zusammenhang mit dem Persönlichkeits- und Datenschutz soll der folgende Beitrag Maßstäbe an eine verfassungs- und datenschutzkonforme technische Gestaltung von Online-Bewertungsportalen aufzeigen.Wed Jan 04 11:31:59 CET 2012it - Information Technology3101-107Enhancing Social Interactions at Conferences.5320112011 COMMUNE conferator itegpub myown rfid sociopatterns Wed Jan 04 11:31:34 CET 2012New YorkRecommender Systems Handbook615--644Social Tagging Recommender Systems20112011 collaborative itegpub myown recommender social tagging The new generation of Web applications known as (STS) is successfully established and poised for continued growth. STS are open and inherently social; features that have been proven to encourage participation. But while STS bring new opportunities, they revive old problems, such as information overload. 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 STS however, we face new challenges. Users are interested in finding not only content, but also tags and even other users. Moreover, while traditional recommender systems usually operate over 2-way data arrays, STS data is represented as a third-order tensor or a hypergraph with hyperedges denoting (user, resource, tag) triples. In this chapter, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve STS.We describe (a) novel facets of recommenders for STS, such as user, resource, and tag recommenders, (b) new approaches and algorithms for dealing with the ternary nature of STS data, and (c) recommender systems deployed in real world STS. Moreover, a concise comparison between existing works is presented, through which we identify and point out new research directions.Wed Jan 04 11:31:03 CET 2012ChemnitzGfA-FrühjahrskongressGestaltung technisch-sozialer Vernetzung in der Arbeitsorganisation: Untersuchung zur Nutzerakzeptanz von RFID-Technologie20112011 conferator itegpub mygroup myown networking rfid social venus Wed Jan 04 11:24:21 CET 2012Working Notes of the LWA 2011 - Learning, Knowledge, AdaptationFace-to-Face Contacts during LWA 2010 - Communities, Roles, and Key Players20112011 LWA analysis conferator contacts itegpub myown Wed Jan 04 11:23:25 CET 2012Proc. Workshop on Mining Ubiquitous and Social Environments (MUSE 2011) at ECML/PKDD 2011Face-to-Face Contacts during a Conference: Communities, Roles, and Key Players20112011 analysis communities community conferator discovery itegpub knowledge myown rfid Wed Jan 04 11:21:23 CET 2012Working Notes of the LWA 2011 - Learning, Knowledge, AdaptationResource-Aware On-Line RFID Localization Using Proximity Data20112011 aware conferator itegpub localization myown rfid Wed Jan 04 11:20:30 CET 2012it - Information Technologyit - Information Technologymay3101--107Enhancing Social Interactions at Conferences5320112011 conferator conference conferences interactions itegpub journal myown Fri Nov 25 12:41:06 CET 2011Berlin/HeidelbergKnowledge Processing and Data Analysis136--149Lecture Notes in Computer ScienceA Comparison of Content-Based Tag Recommendations in Folksonomy Systems658120112011 content folksonomy itegpub l3s myown recommendations recommender tag tagorapub 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. Tue Mar 08 14:10:37 CET 2011Proceedings of the Third International Conference on E-Commerce and Web Technologies (EC-Web 2002), Aix-en-Provence, France304-313LNCSKAON - Towards a large scale Semantic Web245520022002 aifb kaon karlsruhe l3s myown ontologies semantic social web Thu Feb 10 20:54:48 CET 2011Berlin / HeidelbergThe VLDB Journal849-875The social bookmark and publication management system bibsonomy1920102010 BibSonomy Journal VLDB VLDBJ itegpub l3s myown 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.Wed Dec 22 00:32:33 CET 2010Raleigh, NC, USAProceedings of the 19th International World Wide Web Conference (WWW 2010)aprStop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity2010bibsonomy delicious emerge itegpub l3s myown semantic semantics social start tagging thinking web Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.Wed Dec 15 11:43:59 CET 2010Datenbank-Spektrumjun115--24Query Logs as Folksonomies1020102010 folksonomies folksonomy itegpub l3s log logs logsonomy myown 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. Wed Dec 15 11:43:00 CET 2010Toronto, CanadaProceedings of the 21st ACM conference on Hypertext and hypermediaVisit me, click me, be my friend: An analysis of evidence networks of user relationships in Bibsonomy20102010 analysis bibsonomy evidence itegpub l3s links myown networks semantic sna web Wed Dec 15 11:40:56 CET 2010Web Semantics: Science, Services and Agents on the World Wide WebBridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0; The Future of Knowledge Dissemination: The Elsevier Grand Challenge for the Life Sciences2-395 - 96Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0820102010 data introduction itegpub l3s mining myown network semantic social unik web