Conference articles
An analysis of tag-recommender evaluation procedures.
In:
Proceedings of the 7th ACM conference on Recommender systems, series RecSys '13, pages 343-346.
ACM, New York, NY, USA, 2013.
Stephan Doerfel and Robert Jäschke.
[doi]
[abstract]
[BibTeX]
Since the rise of collaborative tagging systems on the web, the tag recommendation task -- suggesting suitable tags to users of such systems while they add resources to their collection -- has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.
Journal articles
Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations.
cs.IR, 1310.1498, 2013.
Nikolas Landia, Stephan Doerfel, Robert Jäschke, Sarabjot Singh Anand, Andreas Hotho and Nathan Griffiths.
[doi]
[abstract]
[BibTeX]
The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.
Miscellaneous
Recommender Systems for Social Tagging Systems.
2012.
L. Balby Marinho, A. Hotho, R. Jäschke, A. Nanopoulos, S. Rendle, L. Schmidt-Thieme, G. Stumme and P. Symeonidis.
[doi]
[abstract]
[BibTeX]
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.
Book chapters
Challenges in Tag Recommendations for Collaborative Tagging Systems.
In:
J. J. Pazos Arias, A. Fernández Vilas and R. P. Díaz Redondo, editors,
Recommender Systems for the Social Web, pages 65-87.
Springer, Berlin/Heidelberg, 2012.
Robert Jäschke, Andreas Hotho, Folke Mitzlaff and Gerd Stumme.
[doi]
[abstract]
[BibTeX]
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.
Conference articles
Tagging with Queries: How and Why?.
In: R. A. Baeza-Yates, P. Boldi, B. A. Ribeiro-Neto and B. B. Cambazoglu, editors,
WSDM (Late Breaking-Results).
ACM, 2009.
Ioannis Antonellis, Hector Garcia-Molina and Jawed Karim.
[doi]
[BibTeX]
Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems.
In:
Proceedings of the 3rd Workshop on Ontology Learning and Population (OLP3).
Patras, Greece, 2008.
Ciro Cattuto, Dominik Benz, Andreas Hotho and Gerd Stumme.
[doi]
[abstract]
[BibTeX]
Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.
Conceptual Clustering of Social Bookmark Sites.
In: A. Hinneburg, editor,
Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007), pages 50-54.
Martin-Luther-Universität Halle-Wittenberg, 2007.
Miranda Grahl, Andreas Hotho and Gerd Stumme.
[doi]
[BibTeX]
Conceptual Clustering of Social Bookmarking Sites.
In:
7th International Conference on Knowledge Management (I-KNOW '07), pages 356-364.
Know-Center, Graz, Austria, 2007.
Miranda Grahl, Andreas Hotho and Gerd Stumme.
[abstract]
[BibTeX]
Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of the
system, but it does not allow a conceptual drill-down, e. g., along a conceptual hierarchy. In this paper, we present a clustering approach for computing such a conceptual hierarchy for a given folksonomy. The hierarchy is complemented with ranked lists of users and resources most related to each cluster. The rankings are computed using our FolkRank algorithm. We have evaluated our approach on large scale data from the del.icio.us bookmarking system.
An Analysis of the Use of Tags in a Blog Recommender System..
In: M. M. Veloso, editor,
IJCAI, pages 2772-2777.
2007.
Conor Hayes, Paolo Avesani and Sriharsha Veeramachaneni.
[doi]
[BibTeX]
Combating spam in tagging systems.
In:
AIRWeb '07: Proc. of the 3rd int. workshop on Adversarial inf. retrieval on the web, pages 57-64.
2007.
Georgia Koutrika, Frans Adjie Effendi, Zoltán Gyöngyi, Paul Heymann and Hector Garcia-Molina.
[BibTeX]
Miscellaneous
Tracking User Attention in Collaborative Tagging Communities.
2007.
Elizeu Santos-Neto, Matei Ripeanu and Adriana Iamnitchi.
[doi]
[BibTeX]
Collaborative Tagging and Semiotic Dynamics.
2006. ttarXiv:cs.CY/0605015.
Ciro Cattuto, Vittorio Loreto and Luciano Pietronero.
[doi]
[abstract]
[BibTeX]
Collaborative tagging has been quickly gaining ground because of its ability to recruit the activity of web users into effectively organizing and sharing vast amounts of information. Here we collect data from a popular system and investigate the statistical properties of tag co-occurrence. We introduce a stochastic model of user behavior embodying two main aspects of collaborative tagging: (i) a frequency-bias mechanism related to the idea that users are exposed to each other's tagging activity; (ii) a notion of memory - or aging of resources - in the form of a heavy-tailed access to the past state of the system. Remarkably, our simple modeling is able to account quantitatively for the observed experimental features, with a surprisingly high accuracy. This points in the direction of a universal behavior of users, who - despite the complexity of their own cognitive processes and the uncoordinated and selfish nature of their tagging activity - appear to follow simple activity patterns.
Conference articles
Das Entstehen von Semantik in BibSonomy.
In:
Social Software in der Wertschöpfung.
Nomos, Baden-Baden, 2006.
Andreas Hotho, Robert Jäschke, Christoph Schmitz and Gerd Stumme.
[doi]
[abstract]
[BibTeX]
Immer mehr Soziale-Lesezeichen-Systeme entstehen im heutigen Web. In solchen Systemen erstellen die Nutzer leichtgewichtige begriffliche Strukturen, so genannte Folksonomies. Ihren Erfolg verdanken sie der Tatsache, dass man keine speziellen Fähigkeiten benötigt, um an der Gestaltung mitzuwirken. In diesem Artikel beschreiben wir unser System BibSonomy. Es erlaubt das Speichern, Verwalten und Austauschen sowohl von Lesezeichen (Bookmarks) als auch von Literaturreferenzen in Form von BibTeX-Einträgen. Die Entwicklung des verwendeten Vokabulars und der damit einhergehenden Entstehung einer gemeinsamen Semantik wird detailliert diskutiert.
TRIAS - An Algorithm for Mining Iceberg Tri-Lattices.
In:
Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 06), pages 907-911.
IEEE Computer Society, Hong Kong, 2006.
Robert Jäschke, Andreas Hotho, Christoph Schmitz, Bernhard Ganter and Gerd Stumme.
[doi]
[BibTeX]
Collaborative Tagging as a Tripartite Network.
In:
Computational Science – ICCS 2006, pages 1114-1117.
Springer Berlin / Heidelberg, 2006.
Renaud Lambiotte and Marcel Ausloos.
[abstract]
[BibTeX]
We describe online collaborative communities by tripartite networks, the nodes being persons, items and tags. We introduce projection methods in order to uncover the structures of the networks, i.e. communities of users, genre families... The structuring of the network is visualised by using a tree representation. The notion of diversity in the system is also discussed.
tagging, communities, vocabulary, evolution.
In:
CSCW '06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work, pages 181-190.
ACM, New York, NY, USA, 2006.
Shilad Sen, Shyong K. Lam, Al Mamunur Rashid, Dan Cosley, Dan Frankowski, Jeremy Osterhouse, F. Maxwell Harper and John Riedl.
[doi]
[abstract]
[BibTeX]
A tagging community's vocabulary of tags forms the basis for social navigation and shared expression.We present a user-centric model of vocabulary evolution in tagging communities based on community influence and personal tendency. We evaluate our model in an emergent tagging system by introducing tagging features into the MovieLens recommender system.We explore four tag selection algorithms for displaying tags applied by other community members. We analyze the algorithms 'effect on vocabulary evolution, tag utility, tag adoption, and user satisfaction.
Technical reports
The Structure of Collaborative Tagging Systems.
Information Dynamics Lab, HP Labs , 2005.
Scott Golder and Bernardo A. Huberman.
[doi]
[BibTeX]
Miscellaneous
The Structure of Collaborative Tagging Systems.
2005.
Scott Golder and Bernardo A. Huberman.
[doi]
[BibTeX]
Collaborative tagging as a tripartite network.
2005. ttarXiv:cs.DS/0512090.
R. Lambiotte and M. Ausloos.
[doi]
[abstract]
[BibTeX]
We describe online collaborative communities by tripartite networks,
the nodes being persons, items and tags. We introduce projection
methods in order to uncover the structures of the networks, i.e.
communities of users, genre families... <br />To do so, we focus
on the correlations between the nodes, depending on their profiles,
and use percolation techniques that consist in removing less correlated
links and observing the shaping of disconnected islands. The structuring
of the network is visualised by using a tree representation. The
notion of diversity in the system is also discussed.