DLGNN: A Double-layer Graph Neural Network Model Incorporating Shopping Sequence Information for Commodity Recommendation

Chuyuan Wei,Jianwei Niu,Yanyan Guo

doi:10.18494/sam.2020.3056

Abstract

DLGNN: A Double-layer Graph Neural Network Model Incorporating Shopping Sequence Information for Commodity Recommendation

Highlights

With the rapid development of mobile Internet, daily acts of people cannot be separated from their mobile devices and internet information. 5G will make connections between people and ISSN 0914-4935 © MYU K.K. https://myukk.org/Sensors and Materials, Vol 32, No 12 (2020)things, and between things and things, that is to say, objects in the home, office, and city will be connected, moving towards systems with wisdom and intelligence
The core function of recommendation systems (RSs) is to predict how likely a user will purchase an item on the basis of historical interactions such as browses, clicks, and purchases.[1]. RSs rely on large datasets of historical data and find a good representation model of historical data, design models and algorithms to predict and recommend
2) We propose the double-layer graph neural network (DLGNN) model, a new recommendation framework based on a graph neural network (GNN), and the nodes of the network are used to express the transition states of users and items

Summary

Introduction

With the rapid development of mobile Internet, daily acts of people cannot be separated from their mobile devices and internet information. 5G will make connections between people and ISSN 0914-4935 © MYU K.K. https://myukk.org/Sensors and Materials, Vol 32, No 12 (2020)things, and between things and things, that is to say, objects in the home, office, and city will be connected, moving towards systems with wisdom and intelligence. As the content and services provided by the Internet become increasingly rich, the large amount of redundant information leads to the problem of information overload and poor user experience. Many online services such as E-commerce, advertising, social networks, and personalized reading are becoming increasingly widespread with the rapid development of mobile Internet. The core function of recommendation systems (RSs) is to predict how likely a user will purchase an item on the basis of historical interactions such as browses, clicks, and purchases.[1] RSs rely on large datasets of historical data and find a good representation model of historical data, design models and algorithms to predict and recommend. The accuracy of algorithms is greatly enhanced using a neuralnetwork-based recommendation model, the effectiveness of the recommendation is not quite as good as expected

Methods

Results

Conclusion