Article ID Journal Published Year Pages File Type
7374872 Physica A: Statistical Mechanics and its Applications 2018 9 Pages PDF
Abstract
For niche recommendation, information filtering has attracted much attention from various fileds. Especially, mass diffusion based models behave prominently. Nevertheless, these models intrinsically suffer superfluous diffusion, transferring redundant preferences to the object and damaging the accuracy, diversity, and personalization of recommendation. Besides, we discover that the symmetrical diffusion can effectively improve recommendation performances. Thus, we assume that the superfluous diffusion should be symmetrically punished. Hence, we propose a symmetrical punishment model on superfluous diffusion for accurate information recommendation. Extensive experiments on two data sets Netflix and Movielens show that our proposed model outperforms mainstream indices remarkably in accuracy, diversity and personalization.
Related Topics
Physical Sciences and Engineering Mathematics Mathematical Physics
Authors
, , ,