Personalization of tagging systems

Abstract

a b s t r a c tSocial media systems have encouraged end user participation in the Internet, for the purpose of storing and distributing Internet content, sharing opinions and maintaining relationships. Collaborative tagging allows users to annotate the resulting user-generated content, and enables effective retrieval of otherwise uncategorised data. However, compared to professional web content production, collaborative tagging systems face the challenge that end-users assign tags in an uncontrolled manner, resulting in unsystematic and inconsistent metadata. This paper introduces a framework for the personalization of social media systems. We pinpoint three tasks that would benefit from personalization: collaborative tagging, collaborative browsing and collaborative search. We propose a ranking model for each task that integrates the individual user's tagging history in the recommendation of tags and content, to align its suggestions to the individual user preferences. We demonstrate on two real data sets that for all three tasks, the personalized ranking should take into account both the user's own preference and the opinion of others.

A dynamic knowledge management framework for the high value manufacturing industry

“…This means that for the Semantic Web to work, online content needs to be codified with meta-tags such as keywords for search-optimised discovery and presentation. Alternatively, it is also possible to allow users to choose their own personalised tags after the content has been uploaded (Wang et al, 2010).…”

Structural Coupling in Web 2.0 Applications

“…Sharing of resources and tags with other users having the same interest offers two major advantages to users: the discovery of knowledge and better ways for searching for information. Tagging systems are being more and more used for users' profiling and in recommender systems [3,4] and for personalization [5].…”

Tags Weighting Based on User Profile

“…For example, based on the fact that exploration based solely on popularity is limited, [17] suggests to offer the user a specific tag clouds and proposes to incorporate the user profile to rank resources by their relevance degrees based on a probabilistic model in the research process. Other works attempt to exploit this user profile in different ways, such as [19] which implements the vector space model, and introduces a user's tags as his interest vector.…”

“…According to [6], it is very common for a user to repeat the same tags already associated with the object, this can make the tag repeated popular without been really relevant to the content. A user need has little chance to be satisfied [17], it can be noise (return of an irrelevant resource because of the association of unrepresentative tags), or silent (omission of a relevant resource because of the non popularity of representative and important tags).…”