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Tag recommendations in folksonomies

by Robert Jäschke, Ro Marinho, Andreas Hotho, Lars Schmidt-thieme, Gerd Stumme - In PKDD , 2007
"... Abstract. Collaborative tagging systems allow users to assign keywords—so called “tags”—to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the pro ..."
Abstract - Cited by 119 (11 self) - Add to MetaCart
the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare two recommendation algorithms on large-scale real life datasets: an adaptation of user-based

Community detection in graphs

by Santo Fortunato , 2009
"... The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of th ..."
Abstract - Cited by 801 (1 self) - Add to MetaCart
The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices

Evaluating collaborative filtering recommender systems

by Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, John T. Riedl - ACM TRANSACTIONS ON INFORMATION SYSTEMS , 2004
"... ..."
Abstract - Cited by 942 (20 self) - Add to MetaCart
Abstract not found

Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network

by Kristina Toutanova , Dan Klein, Christopher D. Manning, Yoram Singer - IN PROCEEDINGS OF HLT-NAACL , 2003
"... We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective ..."
Abstract - Cited by 660 (23 self) - Add to MetaCart
We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii

Linked Data -- The story so far

by Christian Bizer, et al.
"... The term Linked Data refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions of assertion ..."
Abstract - Cited by 700 (14 self) - Add to MetaCart
The term Linked Data refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions

Mutual Contextualization in Tripartite Graphs of Folksonomies

by Ching-man Au Yeung, Nicholas Gibbins, Nigel Shadbolt
"... Abstract. The use of tags to describe Web resources in a collaborative manner has experienced rising popularity among Web users in recent years. The product of such activity is given the name folksonomy, which can be considered as a scheme of organizing information in the users ’ own way. This resea ..."
Abstract - Cited by 6 (0 self) - Add to MetaCart
. This research work attempts to analyze tripartite graphs – graphs involving users, tags and resources – of folksonomies and discuss how these elements acquire their semantics through their associations with other elements, a process we call mutual contextualization. By studying such process, we try to identify

Reversible Markov chains and random walks on graphs

by David Aldous, James Allen Fill , 2002
"... ..."
Abstract - Cited by 549 (13 self) - Add to MetaCart
Abstract not found

Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations

by Jure Leskovec, Jon Kleinberg, Christos Faloutsos , 2005
"... How do real graphs evolve over time? What are “normal” growth patterns in social, technological, and information networks? Many studies have discovered patterns in static graphs, identifying properties in a single snapshot of a large network, or in a very small number of snapshots; these include hea ..."
Abstract - Cited by 534 (48 self) - Add to MetaCart
increase slowly as a function of the number of nodes (like O(log n) orO(log(log n)). Existing graph generation models do not exhibit these types of behavior, even at a qualitative level. We provide a new graph generator, based on a “forest fire” spreading process, that has a simple, intuitive justification

Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions

by Gediminas Adomavicius, Alexander Tuzhilin - IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 2005
"... This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes vario ..."
Abstract - Cited by 1420 (21 self) - Add to MetaCart
This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes

Predictive reward signal of dopamine neurons

by Wolfram Schultz - Journal of Neurophysiology , 1998
"... Schultz, Wolfram. Predictive reward signal of dopamine neurons. is called rewards, which elicit and reinforce approach behav-J. Neurophysiol. 80: 1–27, 1998. The effects of lesions, receptor ior. The functions of rewards were developed further during blocking, electrical self-stimulation, and drugs ..."
Abstract - Cited by 717 (12 self) - Add to MetaCart
Schultz, Wolfram. Predictive reward signal of dopamine neurons. is called rewards, which elicit and reinforce approach behav-J. Neurophysiol. 80: 1–27, 1998. The effects of lesions, receptor ior. The functions of rewards were developed further during blocking, electrical self-stimulation, and drugs
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