A Study of User Profile Generation from Folksonomies

Recommendation systems which aim at providing relevant information to users are becoming more and more important and desirable due to the enormous amount of information available on the Web. Crucial to the performance of a rec- ommendation system is the accuracy of the user proflles used to represent the interests of the users. In recent years, pop- ular collaborative tagging systems such as del.icio.us have aggregated an abundant amount of user-contributed meta- data which provides valuable information about the interests of the users. In this paper, we present our analysis on the personal data in folksonomies, and investigate how accurate user proflles can be generated from this data. We reveal that the majority of users possess multiple interests, and propose an algorithm to generate user proflles which can accurately represent these multiple interests. We also discuss how these user proflles can be used for recommending Web pages and organising personal data.