1,725 research outputs found
Collaborative tagging as a tripartite network
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...
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
Tag-Aware Recommender Systems: A State-of-the-art Survey
In the past decade, Social Tagging Systems have attracted increasing
attention from both physical and computer science communities. Besides the
underlying structure and dynamics of tagging systems, many efforts have been
addressed to unify tagging information to reveal user behaviors and
preferences, extract the latent semantic relations among items, make
recommendations, and so on. Specifically, this article summarizes recent
progress about tag-aware recommender systems, emphasizing on the contributions
from three mainstream perspectives and approaches: network-based methods,
tensor-based methods, and the topic-based methods. Finally, we outline some
other tag-related works and future challenges of tag-aware recommendation
algorithms.Comment: 19 pages, 3 figure
Hypergraph model of social tagging networks
The past few years have witnessed the great success of a new family of
paradigms, so-called folksonomy, which allows users to freely associate tags to
resources and efficiently manage them. In order to uncover the underlying
structures and user behaviors in folksonomy, in this paper, we propose an
evolutionary hypergrah model to explain the emerging statistical properties.
The present model introduces a novel mechanism that one can not only assign
tags to resources, but also retrieve resources via collaborative tags. We then
compare the model with a real-world dataset: \emph{Del.icio.us}. Indeed, the
present model shows considerable agreement with the empirical data in following
aspects: power-law hyperdegree distributions, negtive correlation between
clustering coefficients and hyperdegrees, and small average distances.
Furthermore, the model indicates that most tagging behaviors are motivated by
labeling tags to resources, and tags play a significant role in effectively
retrieving interesting resources and making acquaintance with congenial
friends. The proposed model may shed some light on the in-depth understanding
of the structure and function of folksonomy.Comment: 7 pages,7 figures, 32 reference
Tagging, Folksonomy & Co - Renaissance of Manual Indexing?
This paper gives an overview of current trends in manual indexing on the Web.
Along with a general rise of user generated content there are more and more
tagging systems that allow users to annotate digital resources with tags
(keywords) and share their annotations with other users. Tagging is frequently
seen in contrast to traditional knowledge organization systems or as something
completely new. This paper shows that tagging should better be seen as a
popular form of manual indexing on the Web. Difference between controlled and
free indexing blurs with sufficient feedback mechanisms. A revised typology of
tagging systems is presented that includes different user roles and knowledge
organization systems with hierarchical relationships and vocabulary control. A
detailed bibliography of current research in collaborative tagging is included.Comment: Preprint. 12 pages, 1 figure, 54 reference
Collaborative filtering with diffusion-based similarity on tripartite graphs
Collaborative tags are playing more and more important role for the
organization of information systems. In this paper, we study a personalized
recommendation model making use of the ternary relations among users, objects
and tags. We propose a measure of user similarity based on his preference and
tagging information. Two kinds of similarities between users are calculated by
using a diffusion-based process, which are then integrated for recommendation.
We test the proposed method in a standard collaborative filtering framework
with three metrics: ranking score, Recall and Precision, and demonstrate that
it performs better than the commonly used cosine similarity.Comment: 8 pages, 4 figures, 1 tabl
Comparing the hierarchy of author given tags and repository given tags in a large document archive
Folksonomies - large databases arising from collaborative tagging of items by
independent users - are becoming an increasingly important way of categorizing
information. In these systems users can tag items with free words, resulting in
a tripartite item-tag-user network. Although there are no prescribed relations
between tags, the way users think about the different categories presumably has
some built in hierarchy, in which more special concepts are descendants of some
more general categories. Several applications would benefit from the knowledge
of this hierarchy. Here we apply a recent method to check the differences and
similarities of hierarchies resulting from tags given by independent
individuals and from tags given by a centrally managed repository system. The
results from out method showed substantial differences between the lower part
of the hierarchies, and in contrast, a relatively high similarity at the top of
the hierarchies.Comment: 10 page
Folksonomies and clustering in the collaborative system CiteULike
We analyze CiteULike, an online collaborative tagging system where users
bookmark and annotate scientific papers. Such a system can be naturally
represented as a tripartite graph whose nodes represent papers, users and tags
connected by individual tag assignments. The semantics of tags is studied here,
in order to uncover the hidden relationships between tags. We find that the
clustering coefficient reflects the semantical patterns among tags, providing
useful ideas for the designing of more efficient methods of data classification
and spam detection.Comment: 9 pages, 5 figures, iop style; corrected typo
Measuring Similarity in Large-Scale Folksonomies
Social (or folksonomic) tagging has become a very popular way to describe content within Web 2.0 websites. Unlike\ud
taxonomies, which overimpose a hierarchical categorisation of content, folksonomies enable end-users to freely create and choose the categories (in this case, tags) that best\ud
describe some content. However, as tags are informally de-\ud
fined, continually changing, and ungoverned, social tagging\ud
has often been criticised for lowering, rather than increasing, the efficiency of searching, due to the number of synonyms, homonyms, polysemy, as well as the heterogeneity of\ud
users and the noise they introduce. To address this issue, a\ud
variety of approaches have been proposed that recommend\ud
users what tags to use, both when labelling and when looking for resources. As we illustrate in this paper, real world\ud
folksonomies are characterized by power law distributions\ud
of tags, over which commonly used similarity metrics, including the Jaccard coefficient and the cosine similarity, fail\ud
to compute. We thus propose a novel metric, specifically\ud
developed to capture similarity in large-scale folksonomies,\ud
that is based on a mutual reinforcement principle: that is,\ud
two tags are deemed similar if they have been associated to\ud
similar resources, and vice-versa two resources are deemed\ud
similar if they have been labelled by similar tags. We offer an efficient realisation of this similarity metric, and assess its quality experimentally, by comparing it against cosine similarity, on three large-scale datasets, namely Bibsonomy, MovieLens and CiteULike
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