Multiple Interests of Users in Collaborative Tagging Systems

Abstract

Performance of recommender systems depends on whether the user profiles contain accurate information about the interests of the users, and this in turn relies on whether enough information about their interests can be collected. Collaborative tagging systems allow users to use their own words to describe their favourite resources, resulting in some user-generated categorisation schemes commonly known as folksonomies. Folksonomies thus contain rich information about the interests of the users, which can be used to support various recommender systems. Our analysis of the folksonomy in Delicious reveals that the interests of a single user can be very diverse. Traditional methods for representing interests of users are usually not able to reflect such diversity. We propose a method to construct user profiles of multiple interests from folksonomies based on a network clustering technique. Our evaluation shows that the proposed method is able to generate user profiles which reflect the diversity of user interests and can be used as a basis of providing more focused recommendation to the users.