The Experts below are selected from a list of 3660 Experts worldwide ranked by ideXlab platform
Nigel Shadbolt - One of the best experts on this subject based on the ideXlab platform.
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SPEAR: SPAMMING‐RESISTANT EXPERTISE ANALYSIS AND RANKING IN Collaborative Tagging SYSTEMS
Computational Intelligence, 2011Co-Authors: Chingman Au Yeung, Nicholas Gibbins, Michael G. Noll, Christoph Meinel, Nigel ShadboltAbstract:In this article, we discuss the notions of experts and expertise in resource discovery in the context of Collaborative Tagging systems. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. First, an expert should possess a high-quality collection of resources, while the quality of a Web resource in turn depends on the expertise of the users who have assigned tags to it, forming a mutual reinforcement relationship. Second, an expert should be one who tends to identify interesting or useful resources before other users discover them, thus bringing these resources to the attention of the community of users. We propose a graph-based algorithm, SPEAR (spamming-resistant expertise analysis and ranking), which implements the above ideas for ranking users in a folksonomy. Our experiments show that our assumptions on expertise in resource discovery, and SPEAR as an implementation of these ideas, allow us to promote experts and demote spammers at the same time, with performance significantly better than the original hypertext-induced topic search algorithm and simple statistical measures currently used in most Collaborative Tagging systems.
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spear spamming resistant expertise analysis and ranking in Collaborative Tagging systems
Computational Intelligence, 2011Co-Authors: Chingman Au Yeung, Nicholas Gibbins, Michael G. Noll, Christoph Meinel, Nigel ShadboltAbstract:In this article, we discuss the notions of experts and expertise in resource discovery in the context of Collaborative Tagging systems. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. First, an expert should possess a high-quality collection of resources, while the quality of a Web resource in turn depends on the expertise of the users who have assigned tags to it, forming a mutual reinforcement relationship. Second, an expert should be one who tends to identify interesting or useful resources before other users discover them, thus bringing these resources to the attention of the community of users. We propose a graph-based algorithm, SPEAR (spamming-resistant expertise analysis and ranking), which implements the above ideas for ranking users in a folksonomy. Our experiments show that our assumptions on expertise in resource discovery, and SPEAR as an implementation of these ideas, allow us to promote experts and demote spammers at the same time, with performance significantly better than the original hypertext-induced topic search algorithm and simple statistical measures currently used in most Collaborative Tagging systems.
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user induced links in Collaborative Tagging systems
Conference on Information and Knowledge Management, 2009Co-Authors: Chingman Au Yeung, Nicholas Gibbins, Nigel ShadboltAbstract:Collaborative Tagging systems allow users to use tags to describe their favourite online documents. Two documents that are maintained in the collection of the same user and/or assigned similar sets of tags can be considered as related from the perspective of the user, even though they may not be connected by hyperlinks. We call this kind of implicit relations user-induced links between documents. We consider two methods of identifying user-induced links in Collaborative Tagging, and compare these links with existing hyperlinks on the Web. Our analyses show that user-induced links have great potentials to enrich the existing link structure of the Web. We also propose to use these links as a basis for predicting how documents would be tagged. Our experiments show that they achieve much higher accuracy than existing hyperlinks. This study suggests that by studying the collective behaviour of users we are able to enhance navigation and organisation of Web documents.
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contextualising tags in Collaborative Tagging systems
ACM Conference on Hypertext, 2009Co-Authors: Chingman Au Yeung, Nicholas Gibbins, Nigel ShadboltAbstract:Collaborative Tagging systems are now popular tools for organising and sharing information on the Web. While Collaborative Tagging offers many advantages over the use of controlled vocabularies, they also suffer from problems such as the existence of polysemous tags. We investigate how the different contexts in which individual tags are used can be revealed automatically without consulting any external resources. We consider several different network representations of tags and documents, and apply a graph clustering algorithm on these networks to obtain groups of tags or documents corresponding to the different meanings of an ambiguous tag. Our experiments show that networks which explicitly take the social context into account are more likely to give a better picture of the semantics of a tag.
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On Measuring Expertise in Collaborative Tagging Systems
2009Co-Authors: Chingman Au Yeung, Nicholas Gibbins, Michael G. Noll, Christoph Meinel, Nigel ShadboltAbstract:Collaborative Tagging systems such as Delicious.com provide a new means of organizing and sharing resources. They also allow users to search for documents relevant to a particular topic or for other users who are experts in a particular domain. Nevertheless, identifying relevant documents and knowledgeable users is not a trivial task, especially when the volume of documents is huge and there exist spamming activities. In this paper, we discuss the notions of experts and expertise in the context of Collaborative Tagging systems. We propose that the level of expertise of a user in a particular topic is mainly determined by two factors: (1) there should be a relationship of mutual reinforcement between the expertise of a user and the quality of a document; and (2) an expert should be one who tends to identify useful documents before other users discover them. We propose a graph-based algorithm, SPEAR (SPamming-resistant Expertise Analysis and Ranking), which implements the above ideas for ranking users in a Collaborative Tagging system. We carry out experiments on both simulated data sets and real-world data sets obtained from Delicious, and show that SPEAR is more resistant to spamming than other methods such as the HITS algorithm and simple statistical measures.
Chingman Au Yeung - One of the best experts on this subject based on the ideXlab platform.
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SPEAR: SPAMMING‐RESISTANT EXPERTISE ANALYSIS AND RANKING IN Collaborative Tagging SYSTEMS
Computational Intelligence, 2011Co-Authors: Chingman Au Yeung, Nicholas Gibbins, Michael G. Noll, Christoph Meinel, Nigel ShadboltAbstract:In this article, we discuss the notions of experts and expertise in resource discovery in the context of Collaborative Tagging systems. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. First, an expert should possess a high-quality collection of resources, while the quality of a Web resource in turn depends on the expertise of the users who have assigned tags to it, forming a mutual reinforcement relationship. Second, an expert should be one who tends to identify interesting or useful resources before other users discover them, thus bringing these resources to the attention of the community of users. We propose a graph-based algorithm, SPEAR (spamming-resistant expertise analysis and ranking), which implements the above ideas for ranking users in a folksonomy. Our experiments show that our assumptions on expertise in resource discovery, and SPEAR as an implementation of these ideas, allow us to promote experts and demote spammers at the same time, with performance significantly better than the original hypertext-induced topic search algorithm and simple statistical measures currently used in most Collaborative Tagging systems.
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spear spamming resistant expertise analysis and ranking in Collaborative Tagging systems
Computational Intelligence, 2011Co-Authors: Chingman Au Yeung, Nicholas Gibbins, Michael G. Noll, Christoph Meinel, Nigel ShadboltAbstract:In this article, we discuss the notions of experts and expertise in resource discovery in the context of Collaborative Tagging systems. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. First, an expert should possess a high-quality collection of resources, while the quality of a Web resource in turn depends on the expertise of the users who have assigned tags to it, forming a mutual reinforcement relationship. Second, an expert should be one who tends to identify interesting or useful resources before other users discover them, thus bringing these resources to the attention of the community of users. We propose a graph-based algorithm, SPEAR (spamming-resistant expertise analysis and ranking), which implements the above ideas for ranking users in a folksonomy. Our experiments show that our assumptions on expertise in resource discovery, and SPEAR as an implementation of these ideas, allow us to promote experts and demote spammers at the same time, with performance significantly better than the original hypertext-induced topic search algorithm and simple statistical measures currently used in most Collaborative Tagging systems.
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user induced links in Collaborative Tagging systems
Conference on Information and Knowledge Management, 2009Co-Authors: Chingman Au Yeung, Nicholas Gibbins, Nigel ShadboltAbstract:Collaborative Tagging systems allow users to use tags to describe their favourite online documents. Two documents that are maintained in the collection of the same user and/or assigned similar sets of tags can be considered as related from the perspective of the user, even though they may not be connected by hyperlinks. We call this kind of implicit relations user-induced links between documents. We consider two methods of identifying user-induced links in Collaborative Tagging, and compare these links with existing hyperlinks on the Web. Our analyses show that user-induced links have great potentials to enrich the existing link structure of the Web. We also propose to use these links as a basis for predicting how documents would be tagged. Our experiments show that they achieve much higher accuracy than existing hyperlinks. This study suggests that by studying the collective behaviour of users we are able to enhance navigation and organisation of Web documents.
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contextualising tags in Collaborative Tagging systems
ACM Conference on Hypertext, 2009Co-Authors: Chingman Au Yeung, Nicholas Gibbins, Nigel ShadboltAbstract:Collaborative Tagging systems are now popular tools for organising and sharing information on the Web. While Collaborative Tagging offers many advantages over the use of controlled vocabularies, they also suffer from problems such as the existence of polysemous tags. We investigate how the different contexts in which individual tags are used can be revealed automatically without consulting any external resources. We consider several different network representations of tags and documents, and apply a graph clustering algorithm on these networks to obtain groups of tags or documents corresponding to the different meanings of an ambiguous tag. Our experiments show that networks which explicitly take the social context into account are more likely to give a better picture of the semantics of a tag.
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On Measuring Expertise in Collaborative Tagging Systems
2009Co-Authors: Chingman Au Yeung, Nicholas Gibbins, Michael G. Noll, Christoph Meinel, Nigel ShadboltAbstract:Collaborative Tagging systems such as Delicious.com provide a new means of organizing and sharing resources. They also allow users to search for documents relevant to a particular topic or for other users who are experts in a particular domain. Nevertheless, identifying relevant documents and knowledgeable users is not a trivial task, especially when the volume of documents is huge and there exist spamming activities. In this paper, we discuss the notions of experts and expertise in the context of Collaborative Tagging systems. We propose that the level of expertise of a user in a particular topic is mainly determined by two factors: (1) there should be a relationship of mutual reinforcement between the expertise of a user and the quality of a document; and (2) an expert should be one who tends to identify useful documents before other users discover them. We propose a graph-based algorithm, SPEAR (SPamming-resistant Expertise Analysis and Ranking), which implements the above ideas for ranking users in a Collaborative Tagging system. We carry out experiments on both simulated data sets and real-world data sets obtained from Delicious, and show that SPEAR is more resistant to spamming than other methods such as the HITS algorithm and simple statistical measures.
Alexander Schill - One of the best experts on this subject based on the ideXlab platform.
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rated tags adding rating capability to Collaborative Tagging
International Conference on Cloud and Green Computing, 2013Co-Authors: Daniel Kailer, Peter Mandl, Alexander SchillAbstract:Collaborative Tagging is a popular way to organize content. But sometimes it is also used as a way to express opinions, which is normally done through rating- or text review systems. This paper will demonstrate that there is a gap between rating- and text review systems, that limits the ability of users to find the desired item. A novel concept based on Collaborative Tagging is presented, which is designed to bridge this gap. This concept, named Rated Tags, uses a hybrid approach to combine Tagging-, rating- and review functionality. A key element of Rated Tags is the combination of a traditional user generated tag and a 5-star rating scale. Such a tag is called rated tag. We will discuss the design and the challenges of Rated Tags in detail and demonstrate its application based on a prototype. Furthermore we will show that similar related works suffer from an ambiguity problem, which was avoided in Rated Tags.
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CGC - Rated Tags: Adding Rating Capability to Collaborative Tagging
2013 International Conference on Cloud and Green Computing, 2013Co-Authors: Daniel Kailer, Peter Mandl, Alexander SchillAbstract:Collaborative Tagging is a popular way to organize content. But sometimes it is also used as a way to express opinions, which is normally done through rating- or text review systems. This paper will demonstrate that there is a gap between rating- and text review systems, that limits the ability of users to find the desired item. A novel concept based on Collaborative Tagging is presented, which is designed to bridge this gap. This concept, named Rated Tags, uses a hybrid approach to combine Tagging-, rating- and review functionality. A key element of Rated Tags is the combination of a traditional user generated tag and a 5-star rating scale. Such a tag is called rated tag. We will discuss the design and the challenges of Rated Tags in detail and demonstrate its application based on a prototype. Furthermore we will show that similar related works suffer from an ambiguity problem, which was avoided in Rated Tags.
Nicholas Gibbins - One of the best experts on this subject based on the ideXlab platform.
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SPEAR: SPAMMING‐RESISTANT EXPERTISE ANALYSIS AND RANKING IN Collaborative Tagging SYSTEMS
Computational Intelligence, 2011Co-Authors: Chingman Au Yeung, Nicholas Gibbins, Michael G. Noll, Christoph Meinel, Nigel ShadboltAbstract:In this article, we discuss the notions of experts and expertise in resource discovery in the context of Collaborative Tagging systems. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. First, an expert should possess a high-quality collection of resources, while the quality of a Web resource in turn depends on the expertise of the users who have assigned tags to it, forming a mutual reinforcement relationship. Second, an expert should be one who tends to identify interesting or useful resources before other users discover them, thus bringing these resources to the attention of the community of users. We propose a graph-based algorithm, SPEAR (spamming-resistant expertise analysis and ranking), which implements the above ideas for ranking users in a folksonomy. Our experiments show that our assumptions on expertise in resource discovery, and SPEAR as an implementation of these ideas, allow us to promote experts and demote spammers at the same time, with performance significantly better than the original hypertext-induced topic search algorithm and simple statistical measures currently used in most Collaborative Tagging systems.
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spear spamming resistant expertise analysis and ranking in Collaborative Tagging systems
Computational Intelligence, 2011Co-Authors: Chingman Au Yeung, Nicholas Gibbins, Michael G. Noll, Christoph Meinel, Nigel ShadboltAbstract:In this article, we discuss the notions of experts and expertise in resource discovery in the context of Collaborative Tagging systems. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. First, an expert should possess a high-quality collection of resources, while the quality of a Web resource in turn depends on the expertise of the users who have assigned tags to it, forming a mutual reinforcement relationship. Second, an expert should be one who tends to identify interesting or useful resources before other users discover them, thus bringing these resources to the attention of the community of users. We propose a graph-based algorithm, SPEAR (spamming-resistant expertise analysis and ranking), which implements the above ideas for ranking users in a folksonomy. Our experiments show that our assumptions on expertise in resource discovery, and SPEAR as an implementation of these ideas, allow us to promote experts and demote spammers at the same time, with performance significantly better than the original hypertext-induced topic search algorithm and simple statistical measures currently used in most Collaborative Tagging systems.
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user induced links in Collaborative Tagging systems
Conference on Information and Knowledge Management, 2009Co-Authors: Chingman Au Yeung, Nicholas Gibbins, Nigel ShadboltAbstract:Collaborative Tagging systems allow users to use tags to describe their favourite online documents. Two documents that are maintained in the collection of the same user and/or assigned similar sets of tags can be considered as related from the perspective of the user, even though they may not be connected by hyperlinks. We call this kind of implicit relations user-induced links between documents. We consider two methods of identifying user-induced links in Collaborative Tagging, and compare these links with existing hyperlinks on the Web. Our analyses show that user-induced links have great potentials to enrich the existing link structure of the Web. We also propose to use these links as a basis for predicting how documents would be tagged. Our experiments show that they achieve much higher accuracy than existing hyperlinks. This study suggests that by studying the collective behaviour of users we are able to enhance navigation and organisation of Web documents.
-
contextualising tags in Collaborative Tagging systems
ACM Conference on Hypertext, 2009Co-Authors: Chingman Au Yeung, Nicholas Gibbins, Nigel ShadboltAbstract:Collaborative Tagging systems are now popular tools for organising and sharing information on the Web. While Collaborative Tagging offers many advantages over the use of controlled vocabularies, they also suffer from problems such as the existence of polysemous tags. We investigate how the different contexts in which individual tags are used can be revealed automatically without consulting any external resources. We consider several different network representations of tags and documents, and apply a graph clustering algorithm on these networks to obtain groups of tags or documents corresponding to the different meanings of an ambiguous tag. Our experiments show that networks which explicitly take the social context into account are more likely to give a better picture of the semantics of a tag.
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On Measuring Expertise in Collaborative Tagging Systems
2009Co-Authors: Chingman Au Yeung, Nicholas Gibbins, Michael G. Noll, Christoph Meinel, Nigel ShadboltAbstract:Collaborative Tagging systems such as Delicious.com provide a new means of organizing and sharing resources. They also allow users to search for documents relevant to a particular topic or for other users who are experts in a particular domain. Nevertheless, identifying relevant documents and knowledgeable users is not a trivial task, especially when the volume of documents is huge and there exist spamming activities. In this paper, we discuss the notions of experts and expertise in the context of Collaborative Tagging systems. We propose that the level of expertise of a user in a particular topic is mainly determined by two factors: (1) there should be a relationship of mutual reinforcement between the expertise of a user and the quality of a document; and (2) an expert should be one who tends to identify useful documents before other users discover them. We propose a graph-based algorithm, SPEAR (SPamming-resistant Expertise Analysis and Ranking), which implements the above ideas for ranking users in a Collaborative Tagging system. We carry out experiments on both simulated data sets and real-world data sets obtained from Delicious, and show that SPEAR is more resistant to spamming than other methods such as the HITS algorithm and simple statistical measures.
Michael G. Noll - One of the best experts on this subject based on the ideXlab platform.
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SPEAR: SPAMMING‐RESISTANT EXPERTISE ANALYSIS AND RANKING IN Collaborative Tagging SYSTEMS
Computational Intelligence, 2011Co-Authors: Chingman Au Yeung, Nicholas Gibbins, Michael G. Noll, Christoph Meinel, Nigel ShadboltAbstract:In this article, we discuss the notions of experts and expertise in resource discovery in the context of Collaborative Tagging systems. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. First, an expert should possess a high-quality collection of resources, while the quality of a Web resource in turn depends on the expertise of the users who have assigned tags to it, forming a mutual reinforcement relationship. Second, an expert should be one who tends to identify interesting or useful resources before other users discover them, thus bringing these resources to the attention of the community of users. We propose a graph-based algorithm, SPEAR (spamming-resistant expertise analysis and ranking), which implements the above ideas for ranking users in a folksonomy. Our experiments show that our assumptions on expertise in resource discovery, and SPEAR as an implementation of these ideas, allow us to promote experts and demote spammers at the same time, with performance significantly better than the original hypertext-induced topic search algorithm and simple statistical measures currently used in most Collaborative Tagging systems.
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spear spamming resistant expertise analysis and ranking in Collaborative Tagging systems
Computational Intelligence, 2011Co-Authors: Chingman Au Yeung, Nicholas Gibbins, Michael G. Noll, Christoph Meinel, Nigel ShadboltAbstract:In this article, we discuss the notions of experts and expertise in resource discovery in the context of Collaborative Tagging systems. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. First, an expert should possess a high-quality collection of resources, while the quality of a Web resource in turn depends on the expertise of the users who have assigned tags to it, forming a mutual reinforcement relationship. Second, an expert should be one who tends to identify interesting or useful resources before other users discover them, thus bringing these resources to the attention of the community of users. We propose a graph-based algorithm, SPEAR (spamming-resistant expertise analysis and ranking), which implements the above ideas for ranking users in a folksonomy. Our experiments show that our assumptions on expertise in resource discovery, and SPEAR as an implementation of these ideas, allow us to promote experts and demote spammers at the same time, with performance significantly better than the original hypertext-induced topic search algorithm and simple statistical measures currently used in most Collaborative Tagging systems.
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On Measuring Expertise in Collaborative Tagging Systems
2009Co-Authors: Chingman Au Yeung, Nicholas Gibbins, Michael G. Noll, Christoph Meinel, Nigel ShadboltAbstract:Collaborative Tagging systems such as Delicious.com provide a new means of organizing and sharing resources. They also allow users to search for documents relevant to a particular topic or for other users who are experts in a particular domain. Nevertheless, identifying relevant documents and knowledgeable users is not a trivial task, especially when the volume of documents is huge and there exist spamming activities. In this paper, we discuss the notions of experts and expertise in the context of Collaborative Tagging systems. We propose that the level of expertise of a user in a particular topic is mainly determined by two factors: (1) there should be a relationship of mutual reinforcement between the expertise of a user and the quality of a document; and (2) an expert should be one who tends to identify useful documents before other users discover them. We propose a graph-based algorithm, SPEAR (SPamming-resistant Expertise Analysis and Ranking), which implements the above ideas for ranking users in a Collaborative Tagging system. We carry out experiments on both simulated data sets and real-world data sets obtained from Delicious, and show that SPEAR is more resistant to spamming than other methods such as the HITS algorithm and simple statistical measures.