applications have become more and more popular. Their users record
data during their daily routine or specifically for certain events.
The application WideNoise Plus allows users to record sound samples
and to annotate them with perceptions and tags. The app is being
used to document and map the soundscape all over the world. The procedure
of recording, including the assignment of tags, has to be as easy-to-use
as possible. We therefore discuss the application of tag recommender
algorithms in this particular scenario. We show, that this task is
fundamentally different from the well-known tag recommendation problem
in folksonomies as users do no longer tag fix resources but rather
sensory data and impressions. The scenario requires efficient recommender
algorithms that are able to run on the mobile device, since Internet
connectivity cannot be assumed to be available. Therefore, we evaluate
the performance of several tag recommendation algorithms and discuss
their applicability in the mobile sensing use-case. ER - TY - BOOK AU - Balby Marinho, L. AU - Hotho, A. AU - Jäschke, R. AU - Nanopoulos, A. AU - Rendle, S. AU - Schmidt-Thieme, L. AU - Stumme, G. AU - Symeonidis, P. A2 - T1 - Recommender Systems for Social Tagging Systems PB - Springer C1 - PY - 2012/02 VL - IS - SP - EP - UR - http://link.springer.com/book/10.1007/978-1-4614-1894-8 DO - 10.1007/978-1-4614-1894-8 KW - 2012 KW - bookmarking KW - collaborative KW - folksonomy KW - info20 KW - itegpub KW - l3s KW - myown KW - recommender KW - social KW - tagging KW - tagging KW - 2012 L1 - SN - 978-1-4614-1893-1 N1 - N1 - AB - 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. ER - TY - CHAP AU - Jäschke, Robert AU - Hotho, Andreas AU - Mitzlaff, Folke AU - Stumme, Gerd A2 - Pazos Arias, José J. A2 - Fernández Vilas, Ana A2 - Díaz Redondo, Rebeca P. T1 - Challenges in Tag Recommendations for Collaborative Tagging Systems T2 - Recommender Systems for the Social Web PB - Springer C1 - Berlin/Heidelberg PY - 2012/ VL - 32 IS - SP - 65 EP - 87 UR - http://dx.doi.org/10.1007/978-3-642-25694-3_3 DO - 10.1007/978-3-642-25694-3_3 KW - 2012 KW - bookmarking KW - challenge KW - collaborative KW - dc09 KW - discovery KW - folksonomy KW - info20 KW - itegpub KW - l3s KW - myown KW - recommender KW - rsdc08 KW - social KW - tagging L1 - SN - 978-3-642-25694-3 N1 - N1 - AB - 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. ER - TY - CONF AU - Illig, Jens AU - Hotho, Andreas AU - Jäschke, Robert AU - Stumme, Gerd A2 - Wolff, Karl Erich A2 - Palchunov, Dmitry E. A2 - Zagoruiko, Nikolay G. A2 - Andelfinger, Urs T1 - A Comparison of Content-Based Tag Recommendations in Folksonomy Systems T2 - Knowledge Processing and Data Analysis PB - Springer C1 - Berlin/Heidelberg PY - 2011/ CY - VL - 6581 IS - SP - 136 EP - 149 UR - http://dx.doi.org/10.1007/978-3-642-22140-8_9 DO - 10.1007/978-3-642-22140-8_9 KW - 2011 KW - content KW - folksonomy KW - info20 KW - itegpub KW - l3s KW - myown KW - recommendations KW - recommender KW - tag KW - tagorapub L1 - SN - 978-3-642-22139-2 N1 - N1 - AB - 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.
ER - TY - JOUR AU - Benz, Dominik AU - Hotho, Andreas AU - Jäschke, Robert AU - Krause, Beate AU - Stumme, Gerd T1 - Query Logs as Folksonomies JO - Datenbank-Spektrum PY - 2010/06 VL - 10 IS - 1 SP - 15 EP - 24 UR - http://dx.doi.org/10.1007/s13222-010-0004-8 DO - KW - 2010 KW - folksonomies KW - folksonomy KW - info20 KW - itegpub KW - l3s KW - log KW - logs KW - logsonomy KW - myown L1 - SN - N1 - N1 - AB - 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. ER -