Spectral clustering and embedding-enhanced POI recommendation in location-based social network - Zhen LIU; Na’na WANG; Xiaodong WANG; Yongqi SUN (Tongxin xuebao)
In order to effectively capture the spatial characteristics of multi-dimensional context information in LBSN,and deeply explore the non-linear interaction between users and POIs,a spectral embedding enhanced POI recommendation algorithm,namely PSC-SMLP,was proposed.A preference enhanced spectral clustering algorithm (PSC) and a novel spectral embedded enhanced neural network (SMLP) was designed to solve the above problems.Compared with state-of-the-art algorithms on two datasets,PSC-SMLP has better performance in terms of the precision,recall,nDCG and mean average precision.