A Multi-objective Recommendation Algorithm Based on User Stratification

Hu Xiao-min; Long Zu-tao; Li Min

doi:10.12052/gdutxb.210197

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A Multi-objective Recommendation Algorithm Based on User Stratification

更新时间：2026-05-27

- A Multi-objective Recommendation Algorithm Based on User Stratification
- Vol. 40, Issue 1, Pages: 10-18(2023)
- 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：
- 基金信息：
- DOI：10.12052/gdutxb.210197
  CLC： TP242
- Online First：25 January 2023，
  
  Published：2023
- 稿件说明：
移动端阅览
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

BOBADILLA J, ORTEGA F, HERNANDO A, et al. Recommender systems survey[J]. Knowledge-Based Systems, 2013, 46: 109-132.

WANG N, HE X M, LIU Z Q, et al . Personalized video recommendation strategy based on user’s playback behavior sequence [J ] . Journal of Computer Science, 2020, 43(1): 123-135.

RAE A, MURDOCK V, POPESCU A, et al. Mining the web for points of interest[C]// Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2012: 711-720.

ZHANG Y J, DONG Z, MENG X W. Research on personalized advertising recommendation system and their applications [J]. Journal of Computer Science, 2021, 44(3): 531-563.

LIN S, ZHENG Z H. A research of a recommender system based on customer behavior modeling by mining association rules [J]. Journal of Guangdong University of Technology, 2018, 35(3): 90-94.

YANG J, ZHU X J, ZHOU X Z, et al . Personalized web service recommendation based on heterogeneous social network [J ] . Journal of Electronics, 2020, 48(2): 341-349.

RICCI F, ROKACH L, SHAPIRA B. Introduction to recommender systems handbook[M]. Berlin, Heidelberg: Springer, 2011: 1-35.

CHAI Z Y, LI Y L, HAN Y M, et al. Recommendation system based on singular value decomposition and multi-objective immune optimization[J]. IEEE Access, 2018, 7: 6060-6071.

GU L Z. Research on data sparsity in collaborative filtering[J]. Computer CD Software and Applications, 2014, 17(8) : 101-102.

ZHANG L, QIN T, TENG P. Using key users of social networks to solve cold start problem in collaborative recommendation systems [J]. Information Technology Journal, 2013, 12(22): 7004-7008.

SUN Y, HAN J, YAN X, et al . Path Sim: meta path-based top-k similarity search in heterogeneous information networks [J ] . Proceedings of the VLDB Endowment, 2011, 4(11): 992-1003.

SEDHAIN S, SANNER S, BRAZIUNAS D, et al. Social collaborative filtering for cold-start recommendations[C]//Proceedings of the 8th ACM Conference on Recommender systems. New York: ACM, 2014: 345-348.

BEEL J, LANGER S, GENZMEHR M, et al. Research paper recommender system evaluation: a quantitative literature survey[C]//Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation. New York: ACM, 2013: 15-22.

LIN Q, JIN G, MA Y, et al . A diversity enhanced resource allocation strategy for decomposition-based multi-objective evolutionary algorithm [J ] . IEEE Transactions on Cybernetics, 2018, 48(8): 2388-2401.

FAN J, PAN W, JIANG L. An improved collaborative filtering algorithm combining content-based algorithm and user activity[C] //2014 International Conference on Big Data and Smart Computing (BIGC- OMP) . Bangkok: IEEE, 2014: 88-91.

ADOMAVICIUS G, KWON Y. Improving aggregate recommendation diversity using ranking-based techniques [J]. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(5): 896-911.

CHU P M, TSAI H R, LEE S J, et al. Improving collaborative filtering recommendation[C] //Proceedings of 2018 3rd International Conference on Control, Automation and Artificial Intelligence (CAAI 2018). [S. l. ]: Atlantis Press, 2018: 114-116.

ZHOU T, SU R Q, LIU R R, et al . Accurate and diverse recommendations via eliminating redundant correlations [J ] . New Journal of Physics, 2009, 11(12): 123008.

FANG C, ZHANG H W, WANG N, et al . Personalized service recommendation method based on random walk and diversified graph ranking [J ] . Journal of Electronics, 2018, 46(11): 2773-2780.

WANG P, ZUO X, GUO C, et al. A multi-objective genetic algorithm based hybrid recommendation approach[C]//2017 IEEE Symposium Series on Computational Intelligence (SSCI) . Honolulu: IEEE, 2017: 1-6.

HOU Z, LIU J. A two-phase evolutionary algorithm for solving the accuracy-diversity dilemma in recommendation[C]//2020 IEEE Congress on Evolutionary Computation (CEC) . Glasgow: IEEE, 2020: 1-8.

LIU X F, ZHAN Z H, GAO Y, et al . Coevolutionary particle swarm optimization with bottleneck objective learning strategy for many-objective optimization [J ] . IEEE Transactions on Evolutionary Computation, 2018, 23(4): 587-602.

JAMBOR T, WANG J. Optimizing multiple objectives in collaborative filtering[C] //Proceedings of the 4th ACM Conference on Recommender Systems. New York: ACM, 2010: 55-62.

WANG S, GONG M, LI H, et al. Multi-objective optimization for long tail recommendation[J]. Knowledge-Based Systems, 2016, 104: 145-155.

RIBEIRO M T, LACERDA A, VELOSO A, et al. Pareto-efficient hybridization for multi-objective recommender systems[C] //Proceedings of the 6th ACM Conference on Recommender Systems. New York: ACM, 2012: 19-26.

ZUO Y, GONG M, ZENG J, et al . Personalized recommendation based on evolutionary multi-objective optimization [Research Frontier ] [J ] . IEEE Computational Intelligence Magazine, 2015, 10(1): 52-62.

CUI L, PENG O, FU X, et al. A novel multi-objective evolutionary algorithm for recommendation systems[J]. Journal of Parallel and Distributed Computing, 2017, 103: 53-63.

DEB K, PRATAP A, AGARWAL S, et al . A fast and elitist multi-objective genetic algorithm: NSGA-II [J ] . IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.

HERLOCKER J L, KONSTAN J A, TERVEEN L G, et al . Evaluating collaborative filtering recommender systems [J ] . ACM Transactions on Information Systems (TOIS) , 2004, 22(1): 5-53.

SARWAR B, KARYPIS G, KONSTAN J, et al. Item-based collaborative filtering recommendation algorithms[C]//Proceedings of the 10th International Conference on World Wide Web. New York: ACM, 2001: 285-295.

ZHOU T, REN J, MEDO M, et al . Bipartite network projection and personal recommendation [J ] . Physical Review E, 2007, 76(4): 046115.

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