With the rising popularity of smart mobile devices, sensor data-based
pplications have become more and more popular. Their users record
ata during their daily routine or specifically for certain events.
he application WideNoise Plus allows users to record sound samples
nd to annotate them with perceptions and tags. The app is being
sed to document and map the soundscape all over the world. The procedure
f recording, including the assignment of tags, has to be as easy-to-use
s possible. We therefore discuss the application of tag recommender
lgorithms in this particular scenario. We show, that this task is
undamentally different from the well-known tag recommendation problem
n folksonomies as users do no longer tag fix resources but rather
ensory data and impressions. The scenario requires efficient recommender
lgorithms that are able to run on the mobile device, since Internet
onnectivity cannot be assumed to be available. Therefore, we evaluate
he performance of several tag recommendation algorithms and discuss
heir applicability in the mobile sensing use-case.

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.

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.

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.

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.

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.

In social bookmarking systems users describe bookmarks
keywords called tags. The structure behind
ese social systems, called folksonomies, can be
ewed as a tripartite hypergraph of user, tag and resource
des. This underlying network shows specific
ructural properties that explain its growth and the possibility
serendipitous exploration.
arch engines filter the vast information of the web.
eries describe a user’s information need. In response
the displayed results of the search engine, users click
the links of the result page as they expect the answer
be of relevance. The clickdata can be represented as a
lksonomy in which queries are descriptions of clicked
Ls. This poster analyzes the topological characteristics
the resulting tripartite hypergraph of queries,
ers and bookmarks of two query logs and compares it
o a snapshot of the folksonomy del.icio.us.

Social bookmarking systems allow users to store links to internet resources on a web page. As social bookmarking systems are growing in popularity, search algorithms have been developed that transfer the idea of link-based rankings in the Web to a social bookmarking system’s
ta structure. These rankings differ from traditional search engine rankings in that they incorporate the rating of users.

n this study, we compare search in social bookmarking systems with traditionalWeb search. In the first part, we compare the user activity and behaviour in both kinds of systems, as well as the overlap of the underlying sets of URLs. In the second part,we compare graph-based and vector space rankings for social bookmarking systems with commercial search engine rankings.

ur experiments are performed on data of the social bookmarking system Del.icio.us and on rankings and log data from Google, MSN, and AOL. We will show that part of the difference between the systems is due to different behaviour (e. g., the concatenation of multi-word lexems
single terms in Del.icio.us), and that real-world events may trigger similar behaviour in both kinds of systems. We will also show that a graph-based ranking approach on folksonomies yields results that are closer to the rankings of the commercial search engines than vector space
trieval, and that the correlation is high in particular for the domains that are well covered by the social bookmarking system.

Social bookmarking systems constitute an established
rt of the Web 2.0. In such systems
ers describe bookmarks by keywords
lled tags. The structure behind these social
stems, called folksonomies, can be viewed
a tripartite hypergraph of user, tag and resource
des. This underlying network shows
ecific structural properties that explain its
owth and the possibility of serendipitous
ploration.
day’s search engines represent the gateway
retrieve information from the World Wide
b. Short queries typically consisting of
o to three words describe a user’s information
ed. In response to the displayed
sults of the search engine, users click on
e links of the result page as they expect
e answer to be of relevance.
is clickdata can be represented as a folksonomy
which queries are descriptions of
icked URLs. The resulting network structure,
ich we will term logsonomy is very
milar to the one of folksonomies. In order
find out about its properties, we analyze
e topological characteristics of the tripartite
pergraph of queries, users and bookmarks
a large snapshot of del.icio.us and
query logs of two large search engines.
l of the three datasets show small world
operties. The tagging behavior of users,
ich is explained by preferential attachment
the tags in social bookmark systems, is
flected in the distribution of single query
rds in search engines. We can conclude
at the clicking behaviour of search engine
ers based on the displayed search results
d the tagging behaviour of social bookmarking
ers is driven by similar dynamics.

Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of the
stem, 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.

Social bookmark tools are rapidly emerging on the Web. In such
stems users are setting up lightweight conceptual structures
lled folksonomies. The reason for their immediate success is the
ct that no specific skills are needed for participating. In this
per we specify a formal model for folksonomies and briefly describe
r own system BibSonomy, which allows for sharing both bookmarks
d publication references in a kind of personal library.

Social bookmark tools are rapidly emerging on the Web. In such
stems users are setting up lightweight conceptual structures
lled folksonomies. The reason for their immediate success is the
ct that no specific skills are needed for participating. In this
per we specify a formal model for folksonomies, briefly describe
r own system BibSonomy,
ich allows for sharing both bookmarks and
blication references,
d discuss first steps towards emergent semantics.

Social bookmark tools are rapidly emerging on the Web. In such
stems users are setting up lightweight conceptual structures
lled folksonomies. These systems provide currently relatively few
ructure. We discuss in this paper, how association rule mining
n be adopted to analyze and structure folksonomies, and how the results can be used
r ontology learning and supporting emergent semantics. We
monstrate our approach on a large scale dataset stemming from an
line system.