Modern Information Retrieval
One-class svms for document classification
The Journal of Machine Learning Research
Usage patterns of collaborative tagging systems
Journal of Information Science
Estimating the Support of a High-Dimensional Distribution
Neural Computation
The Benefit of Using Tag-Based Profiles
LA-WEB '07 Proceedings of the 2007 Latin American Web Conference
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
Exploring social annotations for web document classification
Proceedings of the 2008 ACM symposium on Applied computing
One-Class Classification by Combining Density and Class Probability Estimation
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Pattern Matching Techniques to Identify Syntactic Variations of Tags in Folksonomies
WSKS '08 Proceedings of the 1st world summit on The Knowledge Society: Emerging Technologies and Information Systems for the Knowledge Society
Integrating tags in a semantic content-based recommender
Proceedings of the 2008 ACM conference on Recommender systems
Personalized recommendation in social tagging systems using hierarchical clustering
Proceedings of the 2008 ACM conference on Recommender systems
Tag-based filtering for personalized bookmark recommendations
Proceedings of the 17th ACM conference on Information and knowledge management
The Metadata Triumvirate: Social Annotations, Anchor Texts and Search Queries
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Tagommenders: connecting users to items through tags
Proceedings of the 18th international conference on World wide web
Exploring contributions of public resources in social bookmarking systems
Decision Support Systems
Getting the most out of social annotations for web page classification
Proceedings of the 9th ACM symposium on Document engineering
A content-based method to enhance tag recommendation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
IEEE Transactions on Knowledge and Data Engineering
Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions
Artificial Intelligence Review
Web search personalization via social bookmarking and tagging
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Of categorizers and describers: an evaluation of quantitative measures for tagging motivation
Proceedings of the 21st ACM conference on Hypertext and hypermedia
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Tags vs shelves: from social tagging to social classification
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
RED'10 Proceedings of the Third international conference on Resource Discovery
Concurrency and Computation: Practice & Experience
Live digital, remember digital: State of the art and research challenges
Computers and Electrical Engineering
Hi-index | 0.00 |
Social tagging systems allow users to easily create, organize, and share collections of Web resources in a collaborative fashion. Videos, pictures, research papers, and Web pages are shared and annotated in sites such as Del.icio.us, CiteULike, or Flickr, among others. The rising popularity of these systems leads to a constant increase in the number of users actively publishing and annotating resources and, consequently, an exponential growth in the amount of data contained in their folksonomies, the underlying data structure of tagging systems. In turn, the user task of discovering interesting resources becomes more and more difficult and time-consuming. In this paper, the problem of filtering resources from social tagging systems according to individual user interests using purely tagging data is studied. One-class support vector machine classification is evaluated as a means to identify relevant information for users based exclusively on positive examples of their information preferences. It is assumed that users express their interest on resources belonging to a folksonomy by assigning tags to them, whereas there is no straightforward method to collect uninterestingness judgments. Filtering interesting resources based on social tags is an important benefit of exploiting the collective knowledge generated by tagging activities of Web communities. In this paper, the results achieved with tag-based classification are compared with those obtained using more traditional information sources such as the full text of Web pages. Experimental evaluation showed that tag-based classifiers outperformed those learned using the text of documents as well as other content-related sources. Moreover, tag-based classification becomes essential for folksonomies in which no additional content is available because of the nature of resources being stored (e.g., tagging of photos or videos). Copyright © 2012 John Wiley & Sons, Ltd.