Recommendation of Multimedia Items by Link Analysis and Collaborative Filtering
We investigate two recommendation approaches suitable for online multimedia sharing services. Our first approach, UserRank, recommends items by global interestingness irrespective of user preferences and is based on the analysis of ownership and evaluation link structure. We also present a personalized interestingness algorithm that combines UserRank with collaborative filtering which enables a single parameter to control the degree of personalization in the recommendations. Our initial results from an informal user study are encouraging.