广东工业大学学报 ›› 2023, Vol. 40 ›› Issue (01): 10-18.doi: 10.12052/gdutxb.210197

• • 上一篇    下一篇

基于用户分层的多目标推荐算法

胡晓敏1, 龙祖涛1, 李敏1,2   

  1. 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
  • 收稿日期:2021-12-10 出版日期:2023-01-25 发布日期:2023-01-12
  • 通信作者: 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
  • 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
  • 基金资助:
    广东省自然科学基金资助项目(2019A1515011270)；广州市科技计划项目(202007040005)

A Multi-objective Recommendation Algorithm Based on User Stratification

Hu Xiao-min1, Long Zu-tao1, Li Min1,2   

  1. 1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China;
    2. School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2021-12-10 Online:2023-01-25 Published:2023-01-12

HTML

PDF (PC)

1523

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。

关键词: 多目标进化算法, 推荐算法, 用户分层

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.

Key words: multi-objective evolutionary algorithm, recommendation algorithm, user stratification

中图分类号: 

  • TP242
[1] BOBADILLA J, ORTEGA F, HERNANDO A, et al. Recommender systems survey[J]. Knowledge-Based Systems, 2013, 46: 109-132.
[2] 王娜, 何晓明, 刘志强, 等. 一种基于用户播放行为序列的个性化视频推荐策略[J]. 计算机学报, 2020, 43(1): 123-135.
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.
[3] 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.
[4] 张玉洁, 董政, 孟祥武. 个性化广告推荐系统及其应用研究[J]. 计算机学报, 2021, 44(3): 531-563.
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.
[5] 林穗, 郑志豪. 基于关联规则的客户行为建模与商品推荐研究[J]. 广东工业大学学报, 2018, 35(3): 90-94.
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.
[6] 杨洁, 朱咸军, 周献中, 等. 基于混杂社会网络的个性化Web服务推荐方法[J]. 电子学报, 2020, 48(2): 341-349.
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.
[7] RICCI F, ROKACH L, SHAPIRA B. Introduction to recommender systems handbook[M]. Berlin, Heidelberg: Springer, 2011: 1-35.
[8] 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.
[9] 顾立志. 协同过滤数据稀疏性问题研究[J]. 计算机光盘软件与应用, 2014, 17(8) : 101-102.
GU L Z. Research on data sparsity in collaborative filtering[J]. Computer CD Software and Applications, 2014, 17(8) : 101-102.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] 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.
[19] 方晨, 张恒巍, 王娜, 等. 基于随机游走和多样性图排序的个性化服务推荐方法[J]. 电子学报, 2018, 46(11): 2773-2780.
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.
[20] 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.
[21] 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.
[22] 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.
[23] 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.
[24] WANG S, GONG M, LI H, et al. Multi-objective optimization for long tail recommendation[J]. Knowledge-Based Systems, 2016, 104: 145-155.
[25] 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.
[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.
[27] 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.
[28] 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.
[29] 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.
[30] 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.
[31] ZHOU T, REN J, MEDO M, et al. Bipartite network projection and personal recommendation [J]. Physical Review E, 2007, 76(4): 046115.
[1] 周怡璐, 王振友, 李叶紫, 李锋. MOEA/D聚合函数的二次泛化及其优化性能分析[J]. 广东工业大学学报, 2018, 35(04): 37-44.
[2] 彭嘉恩, 邓秀勤, 刘太亨, 刘富春, 李文洲. 融合社交和标签信息的隐语义模型推荐算法[J]. 广东工业大学学报, 2018, 35(04): 45-50.
[3] 胡惠成, 陈平华. 一种融合隐式信任关系的推荐算法[J]. 广东工业大学学报, 2017, 34(03): 43-48.
[4] 江珊, 陈磊, 刘海林. 带有权重偏好的多目标进化算法[J]. 广东工业大学学报, 2016, 33(01): 67-72.
[5] 谢桂芩，杨玉华，涂井先. 带有时间窗的虚拟场站接驳补货车辆路径问题[J]. 广东工业大学学报, 2013, 30(1): 61-67.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2017 《广东工业大学学报》编辑部
地址：广州市东风东路729号广东工业大学 邮编：510090
电话：020-37626653,020-37626139,020-37626396
邮箱：xbzrb@gdut.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发