Academy & Industry Research Collaboration Center (AIRCC)

Volume 11, Number 07, May 2021

Effects of Nonlinear Functions on Knowledge Graph Convolutional Networks
for Recommender Systems with Yelp Knowledge Graph


Xing Wei and Jiangjiang Liu, Lamar University, USA


Knowledge Graph (KG) related recommendation method is advanced in dealing with cold start problems and sparse data. Knowledge Graph Convolutional Network (KGCN) is an end-to-end framework that has been proved to have the ability to capture latent item-entity features by mining their associated attributes on the KG. In KGCN, aggregator plays a key role for extracting information from the high-order structure. In this work, we proposed Knowledge Graph Processor (KGP) for pre-processing data and building corresponding knowledge graphs. A knowledge graph for the Yelp Open dataset was constructed with KGP. In addition, we investigated the impacts of various aggregators with three nonlinear functions on KGCN with Yelp Open dataset KG.


Recommender Systems, Knowledge Graph, Activation Function.