Dynamic Reordering Within the Nearest Neighbor-based Algorithm for Collaborative Filtering
Nenghai Yu · Journal of Chinese Computer Systems · 2011
In the traditional Collaborative Filtering(CF),the most critical component is how to measure the similarity between users.In confront with the problem of data sparsity,traditional CF merely considers the ratings which were rated by both of two users to measure the similarity between them,without fully exploring more information.Meanwhile,some improvements of existing algorithms take into account of the number of co-rated items with introducing the overlap parameters,which need to be manually adjusted,result in the limitation of the algorithm practicality.To address those problems,this paper proposed a collaborative filtering algorithm based on Dynamic Reordering within the Nearest Neighbor set(DRNN).It dynamically adjusts the weight of users in neighbor set according to different target items.In addition,the factor of the modified overlap is introduced to optimize the selection of target user′s neighbors.Empirical studies on dataset MovieLens show that algorithm outperforms other state-of-the-art CF algorithms.