Collaborative filtering algorithm for sparse data sets

This paper presents an algorithm to improve the performance of item-based collaborative filtering algorithms working with sparse data sets.The factors impacting the correlation calculation in item-based collaborative filtering algorithms were analyzed to develop an item relationship density as an important characteristic for describing the rating matrix,the effect of the item relationship density on item-based collaborative filtering is then illustrated.The item relation density is then used to develop a virtual user filling algorithm.That effectively improves the precision and coverage of item-based algorithms with sparse datasets.Thus the item relation density is a key characteristic factor for rating matrices.