基于元图归一化相似性度量的实体推荐

基于异构信息网络(heterogeneous information networks, HIN)中元图的良好表征特性,提出一种结合隐式反馈和PathSim(meta path-based similarity)的归一化相似性度量(normalized similarity measure of meta graph, NSMG),以解决对异构信息网络中高度可见实体的偏好问题。针对Yelp和Amazon数据集构建Yelp-HIN(heterogeneous information networks in Yelp)和Amazon-HIN(heterogeneous information networks in Amazon),定义不同类型的元图及归一化相似度量,使用矩阵分解和因子分解机来组合计算不同元图上的相似性。试验结果表明,基于NSMG的方法在非常稀疏的数据集上性能表现优于常用的HIN实体推荐方法。

Entity recommendation based on normalized similarity measure of meta graph in heterogeneous information network

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.