| Author | Title | Year | Journal/Proceedings | Reftype | DOI/URL |
|---|---|---|---|---|---|
| Balby Marinho, L., Hotho, A., Jäschke, R., Nanopoulos, A., Rendle, S., Schmidt-Thieme, L., Stumme, G. & Symeonidis, P. | Recommender Systems for Social Tagging Systems | 2012 | book | DOIURL | |
| Abstract: 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. | |||||
BibTeX:
@book{balbymarinho2012recommender,
author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.},
title = {Recommender Systems for Social Tagging Systems},
publisher = {Springer},
year = {2012},
url = {http://link.springer.com/book/10.1007/978-1-4614-1894-8},
doi = {http://dx.doi.org/10.1007/978-1-4614-1894-8}
}
|
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| Jäschke, R., Hotho, A., Mitzlaff, F. & Stumme, G. | Challenges in Tag Recommendations for Collaborative Tagging Systems | 2012 | Recommender Systems for the Social Web | incollection | DOIURL |
| Abstract: 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. | |||||
BibTeX:
@incollection{jaeschke2012challenges,
author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd},
title = {Challenges in Tag Recommendations for Collaborative Tagging Systems},
booktitle = {Recommender Systems for the Social Web},
publisher = {Springer},
year = {2012},
volume = {32},
pages = {65--87},
url = {http://dx.doi.org/10.1007/978-3-642-25694-3_3},
doi = {http://dx.doi.org/10.1007/978-3-642-25694-3_3}
}
|
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| Illig, J., Hotho, A., Jäschke, R. & Stumme, G. | A Comparison of Content-Based Tag Recommendations in Folksonomy Systems | 2011 | Knowledge Processing and Data Analysis | inproceedings | DOIURL |
| Abstract: Recommendation algorithms and multi-class classifiers can support ers of social bookmarking systems in assigning tags to their okmarks. Content based recommenders are the usual approach for cing the cold start problem, i.e., when a bookmark is uploaded for e first time and no information from other users can be exploited. this paper, we evaluate several recommendation algorithms in a ld-start scenario on a large real-world dataset. |
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BibTeX:
@inproceedings{illig2009comparison,
author = {Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
title = {A Comparison of Content-Based Tag Recommendations in Folksonomy Systems},
booktitle = {Knowledge Processing and Data Analysis},
publisher = {Springer},
year = {2011},
volume = {6581},
pages = {136--149},
url = {http://dx.doi.org/10.1007/978-3-642-22140-8_9},
doi = {http://dx.doi.org/10.1007/978-3-642-22140-8_9}
}
|
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