Recommender Systems for Social Tagging Systems
(Sprache: Englisch)
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...
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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.
Klappentext zu „Recommender Systems for Social Tagging Systems “
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.
Inhaltsverzeichnis zu „Recommender Systems for Social Tagging Systems “
- Social Tagging Systems- Recommender Systems
- Baseline Techniques
- Advanced Techniques
- Offline Evaluation
- Real World Social Tagging Recommender Systems
- Online Evaluation
- Conclusions
Autoren-Porträt
Prof. Dr. Andreas Hotho ist seit Dezember 2009 Professor an der Universität Würzburg und leitet die Forschungsgruppe für Data Mining und Information Retrieval an der Fakultät für Mathematik und Informatik. Seit 2011 ist er Mitglied des L3S. Er studierte bis 1998 Wirtschaftsinformatik an der Technischen Universität Braunschweig. Von 1999 bis 2004 war er wissenschaftlicher Mitarbeiter am Institut für Angewandte Informatik und Formale Beschreibungsverfahren an der Universität Karlsruhe. Dort promovierte er im Bereich Text Mining, Data Mining und Semantic Web und wendete diese Methoden auch zur Kundensegmentierung bei der Deutschen Telekom AG an. Von April 2004 bis Dezember 2009 war er wissenschaftlicher Assistent an der Universität Kassel und beschäftigte sich dort u.a. mit den Themen Semantic Web Mining, Ontology Learning und Web 2.0 im speziellen Social-Bookmarking- und Tagging-Systeme. Seit Ende 2005 leitet er die Entwicklung des bekannten Publikationsverwaltungssystems BibSonomy. Aktuell forscht er im Bereich Web Science mit Fokus auf der Analyse von Daten aus sozialen Netzen und Sensor Daten, die in ubiquitären Systemen in Kombination mit Nutzerinformationen entstehen. In der Vergangenheit organisierte er verschiedene Workshops auf der Schnittstelle zwischen Semantic Web, Web 2.0 und Data Mining, häufig in Verbindung mit den Tagungen ECML PKDD, KDD und ESWC.
Bibliographische Angaben
- 2012, 2012, IX, 111 Seiten, Maße: 15,3 x 24 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 1461418933
- ISBN-13: 9781461418931
Sprache:
Englisch
Pressezitat
From the reviews:"The book is a useful contribution towards harnessing crowd sourced descriptions by using recommender systems as it epitomises the long experience of the majority of the authors in social tagging systems ... . the engaged researcher in the area will benefit from reading this book in that it provides a good orientation over state-of-the-art approaches and techniques for building recommender systems in order to harness the innate variety of crowd sourced annotations and tags." (Cathal Gurrin, Informer, July, 2013)
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