%A Wenkai ZHANG,Ke YU,Xiaofei WU %T Entity recommendation based on normalized similarity measure of meta graph in heterogeneous information network %0 Journal Article %D 2020 %J Journal of Shandong University(Engineering Science) %R 10.6040/j.issn.1672-3961.0.2019.304 %P 66-75 %V 50 %N 2 %U {http://gxbwk.njournal.sdu.edu.cn/CN/abstract/article_1911.shtml} %8 2020-04-20 %X

Based on the promising result of meta graph in heterogeneous information networks (HIN), normalized similarity measure of meta graph (NSMG) was proposed which combined implicit feedback matrix and PathSim(meta path-based similarity) to solve the problem of preference for large degree entities. Yelp-HIN(heterogeneous information networks in Yelp) and Amazon-HIN(heterogeneous information networks in Amazon) were constructed based on Yelp and Amazon datasets. Different types of meta graphs and normalized similarity measures were defined. Matrix decomposition and factorization machine were used to combine the similarities on different meta graphs. The experimental results showed that the proposed method based on normalization similarity measure of meta graphs performed better than the commonly used entity recommendation method in HIN on very sparse data sets.