Social View Based User Modeling for Recommendation in Tagging Systems by Association Rules
Keqin He, Liang He, Xin Lin, Wei Lü · 2010
Social tagging systems such as Facebook, YouTube, del.icio.us, Flickr become popular recent years and have achieved widespread success. State-of-art user modeling approaches in tagging systems usually use a vector of weighted tags. Unfortunately, typical user modeling methods using a vector of weighted tags which are based on personal view only and ignore the social view, have some inherent drawbacks. As in a social network like collaborative tagging system, it is subjective and incomplete to profile using only personal view. In this paper, a novel approach applying association rules is proposed to extend user profiles from the social view. The enriched user profile is a harvest from both personal view and social view. Algorithms of personalized recommendations for tags and items are presented. Also experimental results of using the profile we proposed are discussed.