基于异构网络表示学习的评分预测模型

doi:10.13700/j.bh.1001-5965.2020.0100

北京航空航天大学学报 ›› 2021, Vol. 47 ›› Issue (5): 1077-1084.doi: 10.13700/j.bh.1001-5965.2020.0100

• 论文 • 上一篇

基于异构网络表示学习的评分预测模型

詹娜娜^1,2, 刘伟³, 陈新波⁴, 蒲菊华^1,2

1. 深圳北航新兴产业技术研究院, 深圳 518057;
2. 北京航空航天大学计算机学院, 北京 100083;
3. 北京航空航天大学经济管理学院, 北京 100083;
4. 中国北方车辆研究所, 北京 100072

收稿日期:2020-03-19 发布日期:2021-05-28
通讯作者: 刘伟 E-mail:wayne@buaa.edu.cn
作者简介:詹娜娜,女,硕士研究生。主要研究方向:数据挖掘;刘伟,男,博士研究生,助理研究员。主要研究方向:数据分析和信息系统;陈新波,男,博士。主要研究方向:数据挖掘;蒲菊华,女,博士,副教授,博士生导师。主要研究方向:城市计算与智慧城市。
基金资助:
国家重点研发计划（2017YFB1002000）；深圳市基础研究项目（JCYJ20180307123659504）

Rating prediction model based on heterogeneous network representation learning

ZHAN Nana^1,2, LIU Wei³, CHEN Xinbo⁴, PU Juhua^1,2

1. Research Institute of Beihang University in Shenzhen, Shenzhen 518057, China;
2. School of Computer Science and Engineering, Beihang University, Beijing 100083;
3. School of Economics and Management, Beihang University, Beijing 100083, China;
4. China North Vehicle Research Institute, Beijing 100072, China

Received:2020-03-19 Published:2021-05-28

摘要/Abstract

摘要： 深入分析电商行业的用户个性化数据并提供推荐服务近年来已成为业界的热点。推荐服务的基础是对用户的潜在兴趣进行挖掘，并对商品的感兴趣程度进行预测。因此，以此为背景，研究用户对商品的评分预测。对电商业的关系型数据在推荐系统中的应用进行了研究，提出了通过使用网络表示学习进行评分预测的方法。首先，将关系型数据构建成异构网络，用户和商品为网络中的节点。然后，设计了兼顾网络结构信息和节点之间相似性的个性化异构网络采样方法，并对节点进行表示学习。最后，将学习到的用户、商品表示向量输入到神经网络中进行训练，利用优化后的神经网络模型进行评分预测。实验结果表明：所提方法在YELP 13、Movielens 100k、Movielens 1m数据集上都有较高的准确率，对比常用方法，准确率提升6.5%以上。

关键词: 评分预测, 推荐系统, 表示学习, 随机游走, 神经网络

Abstract: In recent years, it has become a hot spot to deeply analyze the personalized data of e-commerce users and provide recommendation services.The basis of recommendation service is to mine the potential interest of users and predict user's interest of products. Therefore, this paper takes this as the background to study the user's rating prediction of products. This paper studies the application of relational data of e-commerce in recommendation system, and puts forward a method of rating prediction by using network representation learning. First, the relational data is constructed into a heterogeneous network, and the users and items are the nodes in the network. Then, a personalized heterogeneous network sampling method is designed, which takes into account the network structure information and the similarity between nodes, and the nodes are represented and learned. Finally, the learned user and items representation vectors are input into the neural network for training, and the optimized neural network model is used to predict the score. The experimental results show that this method has high accuracy on YELP 13, Movielens 100k and Movielens 1m datasets. Compared with common methods, the accuracy is improved by more than 6.5%.

Key words: rating prediction, recommendation system, representation learning, random walk, neural network

中图分类号:

TP391

詹娜娜, 刘伟, 陈新波, 蒲菊华. 基于异构网络表示学习的评分预测模型[J]. 北京航空航天大学学报, 2021, 47(5): 1077-1084.

ZHAN Nana, LIU Wei, CHEN Xinbo, PU Juhua. Rating prediction model based on heterogeneous network representation learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(5): 1077-1084.

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基于异构网络表示学习的评分预测模型

Rating prediction model based on heterogeneous network representation learning

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