考虑多层次潜在特征的个性化推荐模型

申情; 郭文宾; 楼俊钢; 余强国

doi:10.11959/j.issn.1000-0801.2022035

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考虑多层次潜在特征的个性化推荐模型

研究与开发 | 更新时间：2024-06-05

- 考虑多层次潜在特征的个性化推荐模型
- Personalized recommendation model with multi-level latent features
- 电信科学 2022年38卷第2期页码：71-83
- 作者机构：
  
  1. 湖州师范学院信息工程学院，浙江湖州 313000
  2. 湖州学院理工学院，浙江湖州 313000
  3. 浙江省现代农业资源智慧管理与应用研究重点实验室，浙江湖州 313000
- 作者简介：
  
  [ "申情（1982- ），女，湖州学院副教授，主要研究方向为个性化推荐、多目标优化、智能决策等" ]
  [ "郭文宾（1996- ），男，湖州师范学院信息工程学院硕士生，主要研究方向为个性化推荐" ]
  [ "楼俊钢（1982- ），男，博士，湖州师范学院信息工程学院教授，主要研究方向为智能信息处理、个性化推荐" ]
  [ "余强国（1977- ），男，湖州学院理工学院高级工程师，主要从事模式识别和智能控制等" ]
- 基金信息：
  
  浙江省重点研发计划项目;The Key Research and Development Program of Zhejiang Province(2020C01097)
- DOI：10.11959/j.issn.1000-0801.2022035
  中图分类号： TP391.3
- 网络出版日期：2022-02，
  
  纸质出版日期：2022-02-20
- 稿件说明：
移动端阅览
申情, 郭文宾, 楼俊钢, 等. 考虑多层次潜在特征的个性化推荐模型[J]. 电信科学, 2022,38(2):71-83.

Qing SHEN, Wenbin GUO, Jungang LOU, et al. Personalized recommendation model with multi-level latent features[J]. Telecommunications science, 2022, 38(2): 71-83.
申情, 郭文宾, 楼俊钢, 等. 考虑多层次潜在特征的个性化推荐模型[J]. 电信科学, 2022,38(2):71-83. DOI： 10.11959/j.issn.1000-0801.2022035.

Qing SHEN, Wenbin GUO, Jungang LOU, et al. Personalized recommendation model with multi-level latent features[J]. Telecommunications science, 2022, 38(2): 71-83. DOI： 10.11959/j.issn.1000-0801.2022035.

摘要

个性化推荐已成为解决信息过载的最有效手段之一，也是海量数据挖掘研究领域的热点技术。然而传统推荐算法往往只使用用户对物品的评分信息，而缺少对用户与物品潜在特征的综合考虑。基于因子分解机、宽神经网络、交叉网络和深度神经网络的融合，提出一种新的考虑多层次潜在特征的模型，可以提取用户与物品的浅层潜在特征、低阶非线性潜在特征、线性交叉潜在特征以及高阶非线性潜在特征。在 4 个常用的数据集上的实验结果表明，考虑用户与物品多层次潜在特征可以有效提高个性化推荐的预测精度。最后，研究了嵌入层维度以及神经元数量等因素对新模型预测性能的影响。

Abstract

Personalized recommendation has become one of the most effective means to solve information overload

and it is also a hot technology in the research field of massive data mining.However

traditional recommendation algorithms often only use the user’s rating information on the item

and lack a comprehensive consideration of the potential characteristics of the user and the item.The factorization machine

wide neural network

crossover network and deep neural network were combined to extract the shallow latent features

low-order nonlinear latent features

linear cross latent features

and high-order nonlinear latent features of users and items.Thus

a new deep learning personalized recommendation model with multilevel latent features was established.The experimental results on four commonly used data sets show that considering the multi-level potential features of users and items can effectively improve the prediction accuracy of personalized recommendations.Finally

the influence of factors such as the dimensions of the embedding layer and the number of neurons on the prediction performance of the new model was studied.

关键词

Keywords

references

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