@inproceedings{kowald2014probability, abstract = {In this paper, we introduce a tag recommendation algorithm that mimics the way humans draw on items in their long-term memory. This approach uses the frequency and recency of previous tag assignments to estimate the probability of reusing a particular tag. Using three real-world folksonomies gathered from bookmarks in BibSonomy, CiteULike and Flickr, we show how incorporating a time-dependent component outperforms conventional "most popular tags" approaches and another existing and very effective but less theory-driven, time-dependent recommendation mechanism. By combining our approach with a simple resource-specific frequency analysis, our algorithm outperforms other well-established algorithms, such as FolkRank, Pairwise Interaction Tensor Factorization and Collaborative Filtering. We conclude that our approach provides an accurate and computationally efficient model of a user's temporal tagging behavior. We demonstrate how effective principles of information retrieval can be designed and implemented if human memory processes are taken into account.}, acmid = {2576934}, address = {Republic and Canton of Geneva, Switzerland}, author = {Kowald, Dominik and Seitlinger, Paul and Trattner, Christoph and Ley, Tobias}, booktitle = {Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion}, doi = {10.1145/2567948.2576934}, interhash = {911a425fa4f6e883c32fa7a09840bdd8}, intrahash = {659fca43cb6751ee9b13297b797d91e1}, isbn = {978-1-4503-2745-9}, location = {Seoul, Korea}, numpages = {6}, pages = {463--468}, publisher = {International World Wide Web Conferences Steering Committee}, series = {WWW Companion '14}, title = {Long Time No See: The Probability of Reusing Tags As a Function of Frequency and Recency}, url = {http://dx.doi.org/10.1145/2567948.2576934}, year = 2014 } @inproceedings{krause2008antisocial, abstract = {The annotation of web sites in social bookmarking systems has become a popular way to manage and find information on the web. The community structure of such systems attracts spammers: recent post pages, popular pages or specific tag pages can be manipulated easily. As a result, searching or tracking recent posts does not deliver quality results annotated in the community, but rather unsolicited, often commercial, web sites. To retain the benefits of sharing one's web content, spam-fighting mechanisms that can face the flexible strategies of spammers need to be developed. A classical approach in machine learning is to determine relevant features that describe the system's users, train different classifiers with the selected features and choose the one with the most promising evaluation results. In this paper we will transfer this approach to a social bookmarking setting to identify spammers. We will present features considering the topological, semantic and profile-based information which people make public when using the system. The dataset used is a snapshot of the social bookmarking system BibSonomy and was built over the course of several months when cleaning the system from spam. Based on our features, we will learn a large set of different classification models and compare their performance. Our results represent the groundwork for a first application in BibSonomy and for the building of more elaborate spam detection mechanisms.}, acmid = {1451998}, address = {New York, NY, USA}, author = {Krause, Beate and Schmitz, Christoph and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 4th International Workshop on Adversarial Information Retrieval on the Web}, doi = {10.1145/1451983.1451998}, interhash = {a45d40ac7776551301ad9dde5b25357f}, intrahash = {50c22098a7a85b1e43e7e4df1d8a3e7a}, isbn = {978-1-60558-159-0}, location = {Beijing, China}, numpages = {8}, pages = {61--68}, publisher = {ACM}, series = {AIRWeb '08}, title = {The Anti-social Tagger: Detecting Spam in Social Bookmarking Systems}, url = {http://doi.acm.org/10.1145/1451983.1451998}, year = 2008 } @incollection{mitzlaff2011community, abstract = {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.}, author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Analysis of Social Media and Ubiquitous Data}, doi = {10.1007/978-3-642-23599-3_5}, editor = {Atzmueller, Martin and Hotho, Andreas and Strohmaier, Markus and Chin, Alvin}, interhash = {1ef065a81ed836dfd31fcc4cd4da133b}, intrahash = {a1c0fd5a9f8c5ddb33b3196927409f36}, isbn = {978-3-642-23598-6}, language = {English}, pages = {79-98}, publisher = {Springer Berlin Heidelberg}, series = {Lecture Notes in Computer Science}, title = {Community Assessment Using Evidence Networks}, url = {http://dx.doi.org/10.1007/978-3-642-23599-3_5}, volume = 6904, year = 2011 } @inproceedings{stiller2011tagging, abstract = {This paper investigates the occurrence of tags in different languages in a collaborative bookmarking and publication sharing service - BibSonomy. Social tags assigned to URLs in multiple languages and users tagging these URLs multilingually are the main focus of this study. The results show that multilingual tags occur for the same URL and that users tag in different languages. Furthermore, the results give indications that the language of the content of a URL does not imply that its tags are in the same language.}, acmid = {1998165}, address = {New York, NY, USA}, author = {Stiller, Juliane and G\"{a}de, Maria and Petras, Vivien}, booktitle = {Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries}, doi = {10.1145/1998076.1998165}, interhash = {9aa32080ecded18e2b08c352ef6fc1a0}, intrahash = {bc4f408e005fc412ac8d2b1b81b5f4a9}, isbn = {978-1-4503-0744-4}, location = {Ottawa, Ontario, Canada}, numpages = {2}, pages = {421--422}, publisher = {ACM}, series = {JCDL '11}, title = {Is Tagging Multilingual?: A Case Study with BibSonomy}, url = {http://doi.acm.org/10.1145/1998076.1998165}, year = 2011 } @article{noy2008challenge, abstract = {The great success of Web 2.0 is mainly fuelled by an infrastructure that allows web users to create, share, tag, and connect content and knowledge easily. The tools for developing structured knowledge in this manner have started to appear as well. However, there are few, if any, user studies that are aimed at understanding what users expect from such tools, what works and what doesn't. We organized the Collaborative Knowledge Construction (CKC) Challenge to assess the state of the art for the tools that support collaborative processes for creation of various forms of structured knowledge. The goal of the Challenge was to get users to try out different tools and to learn what users expect from such tools-features that users need, features that they like or dislike. The Challenge task was to construct structured knowledge for a portal that would provide information about research. The Challenge design contained several incentives for users to participate. Forty-nine users registered for the Challenge; thirty-three of them participated actively by using the tools. We collected extensive feedback from the users where they discussed their thoughts on all the tools that they tried. In this paper, we present the results of the Challenge, discuss the features that users expect from tools for collaborative knowledge constructions, the features on which Challenge participants disagreed, and the lessons that we learned.}, author = {Noy, N F and Chugh, A and Alani, H}, doi = {10.1109/MIS.2008.14}, interhash = {df2e2abfd18d3b415d4b6a7cac970286}, intrahash = {98dcb79390913054e6255e605223f4b2}, journal = {IEEE Intell Syst}, month = {1}, number = 1, pages = {64-68}, pmid = {24683367}, title = {The CKC Challenge: Exploring Tools for Collaborative Knowledge Construction}, url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3966208/}, volume = 23, year = 2008 }