基于用户分层的多目标推荐算法

胡晓敏; 龙祖涛; 李敏

doi:10.12052/gdutxb.210197

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基于用户分层的多目标推荐算法

更新时间：2026-05-27

- 基于用户分层的多目标推荐算法
- A Multi-objective Recommendation Algorithm Based on User Stratification
- 广东工业大学学报 2023年40卷第1期页码：10-18
- 作者机构：
  
  1. 广东工业大学计算机学院,广东,广州,510006
  2. 广东工业大学信息工程学院,广东,广州,510006
- 作者简介：
  
  胡晓敏(1983–)，女，副教授，博士，硕士生导师，主要研究方向为计算智能、数据挖掘
  李敏(1978–)，女，博士研究生，主要研究方向为人工智能、图像处理，E-mail：lmjsj@gdut.edu.cn
- 基金信息：
  
  广东省自然科学基金资助项目(2019A1515011270);广州市科技计划项目(202007040005)
- DOI：10.12052/gdutxb.210197
  中图分类号： TP242
- 网络首发：2023-01-25，
  
  纸质出版：2023
- 稿件说明：
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胡晓敏, 龙祖涛, 李敏. 基于用户分层的多目标推荐算法[J]. 广东工业大学学报, 2023, 40(1): 10-18.

Hu Xiao-min, Long Zu-tao, Li Min. A Multi-objective Recommendation Algorithm Based on User Stratification[J]. 2023, 40(1): 10-18.
胡晓敏, 龙祖涛, 李敏. 基于用户分层的多目标推荐算法[J]. 广东工业大学学报, 2023, 40(1): 10-18. DOI： 10.12052/gdutxb.210197.

Hu Xiao-min, Long Zu-tao, Li Min. A Multi-objective Recommendation Algorithm Based on User Stratification[J]. 2023, 40(1): 10-18. DOI： 10.12052/gdutxb.210197.

摘要

经典的推荐系统着重于推荐的准确性，随着用户多样化需求的增加，推荐结果的多样性受到越来越多的关注。推荐的精度与多样性存在冲突，传统的推荐算法往往忽略系统中的用户活跃度差异。本文提出一种基于物品评价次数的用户分层多目标推荐算法，将用户分为评价次数高、中、低三种层次，对应三种不同的算法初始化方式，为不同用户提供更合适的推荐结果。对已有基于概率的多目标进化算法的初始化方式和参数进行对比分析，获得更优的算法交叉和变异方式。实验结果验证了改进后的多目标进化算法在推荐精度和多样性方面都有更优的结果。总结出的基于用户分层的推荐方案有助于提高对不同用户的推荐效果。

Abstract

The classic recommendation system focuses on the accuracy of recommendation. With the increase of users’ diversified needs

the diversity of recommendation results has attracted more and more attention. The accuracy and diversity of the recommendation always conflict with each other

and the traditional recommendation algorithms often ignore the difference of users’ activity in the system. A user stratification multi-objective recommendation algorithm is proposed based on the difference of users’ evaluation times on the items

which can provide better recommendation results for different users. Users are divided into three types with high

medium and low evaluation times

and three different initialization methods are proposed for the algorithm. By the comparative analysis of the initialization operations and parameter settings of the existing probability based multi-objective evolutionary algorithm

improved crossover and mutation operations are obtained. The experimental results verify that the enhanced multi-objective evolutionary algorithm can find better results with higher accuracy and diversity. Finally

a recommendation scheme based on user stratification is summarized

which helps to improve the recommendation effect for different users.

关键词

Keywords

references

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