A self-adapted method for the categorization of social resources

Social tagging systems have become a popular system to organize information in many web 2.0 sites. They are also being rapidly adopted in enterprises to enhance information sharing, knowledge sharing and emerged as a novel categorization scheme based on the collective knowledge of people.Scalability is an issue of the categorization of the resources of social tagging systems. Scalability has highlighted a critical trade-off between accuracy and complexity. As social tagging systems evolve over time, resource categories can appear or disappear either by grouping new resources or disaggregating existing ones, and this implies the re-assignation of the resources involved to others categories. This makes the methods and/or algorithms that categorize resources of social tagging systems to be non-scalable, and then not efficiently implementable on real social tagging systems. This paper presents a simple method for categorizing resources on social tagging systems which is self-adaptive, scalable and implementable in any real social tagging system.

► Simple, self-adaptive and scalable method for categorizing resources on social tagging systems.
► Significantly lower cost than the required when considering all the tags of the social tagging system.
► SAM’s self-adaptation and scalability is reached by means of incremental computation.
► It does not require a full reassessment each time the social tagging system evolves.
► Its reduced computational cost allows its applications to different real social tagging systems.