Improved recommendation system via propagated neighborhoods based collaborative filtering

Abstract

In this paper, a new two levels propagated neighborhoods based collaborative filtering method (PNCF) is proposed for developing effective and efficient recommendation system. Traditional collaborative filtering (CF) algorithms focus on construct k-nearest neighborhood for each item/user from user-item purchase/rating matrix, such as item-based k-nearest-neighbor collaborative filtering method (itemKNN) and user-based k-nearest-neighbor collaborative filtering method (userKNN). However, the utilization of K-nearest neighborhood method for singe item/user always misses some nature neighbors due to inevitable data noise and data sparsity, resulting in poor prediction accuracy. A novel two levels propagated neighborhoods construction strategy is introduced in PNCF to complement traditional K-nearest neighborhood method, uncovering the underlying neighborhood relationship of each data sample. Furthermore, utilizing propagated neighborhoods improves the recommendation quality. Numerous experiments on MovieLens data set show the superiority of our approach over current state of the art recommendation methods.