Research on the graphical convolution neural network based benefits recommendation system strategy

Tao TAO; Zhen LI; Jibin WANG; Haiyong XU; Yong JIANG; Zhuo CEHN; Runbo ZHANG; Qingyuan HU

doi:10.11959/j.issn.1000-0801.2023155

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Research on the graphical convolution neural network based benefits recommendation system strategy

Research and Development | 更新时间：2024-06-05

- Research on the graphical convolution neural network based benefits recommendation system strategy
- Telecommunications Science Vol. 39, Issue 8, Pages: 91-101(2023)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2023155
  CLC： TP399
- Published Online：2023-08，
  
  Published：20 August 2023
- 稿件说明：
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Tao TAO, Zhen LI, Jibin WANG, et al. Research on the graphical convolution neural network based benefits recommendation system strategy[J]. Telecommunications science, 2023, 39(8): 91-101.
DOI：

Tao TAO, Zhen LI, Jibin WANG, et al. Research on the graphical convolution neural network based benefits recommendation system strategy[J]. Telecommunications science, 2023, 39(8): 91-101. DOI： 10.11959/j.issn.1000-0801.2023155.

摘要

推荐系统是实现海量互联网权益产品智能化推荐的重要手段。为了提升个性化推荐的准确率，提出了基于图计算方法的深度学习推荐系统。针对用户行为数据存在多源异质的特性，基于深度学习图表示技术，对用户多维特征及权益产品之间的多种交互方式进行图结构化信息抽取及异质图建模，构建用户权益多元关系图谱，实现了各类交互信息（如用户—App、App—套餐、用户—套餐）的有效聚合。通过构建异质图卷积神经网络，学习各类异质性节点的高维特征向量，挖掘用户潜在偏好行为，提供具有较强可解释性的推荐链路，进而大幅提升推荐成功率并产生经济价值。

Abstract

The recommendation system is one of the important methods to realize the intelligent recommendation of massive Internet benefit products.In order to improve the accuracy of personalized benefits recommendation

a deep learning recommendation system based on graph computing method was proposed.Considering the heterogeneity of multi-source data

a graph representation technology based on deep learning was carried out to construct the multiple relationship graph between users and benefit products.The multiple relationship graph extracted the information of graph structure

and model the heterogeneous graphs for the multi-dimensional features of users and the multiple interaction modes between rights and interests products

which effectively aggregated various interactive information and the multiple feature.A heterogeneous graph convolutional neural network was built to learn the high-dimensional feature vectors for various nodes

and excavate users＇ latent preferences to provide a recommendation link with strong interpretability

which greatly improved the recommendation success rate and generating economic value.

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Keywords

references

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