Please wait a minute...
Advanced Search
现代图书情报技术  2015, Vol. 31 Issue (6): 13-19     https://doi.org/10.11925/infotech.1003-3513.2015.06.03
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于用户分类的协同过滤个性化推荐方法研究
祝婷, 秦春秀, 李祖海
西安电子科技大学经济与管理学院 西安 710071
Research on Collaborative Filtering Personalized Recommendation Method Based on User Classification
Zhu Ting, Qin Chunxiu, Li Zuhai
School of Economics and Management, Xidian University, Xi'an 710071, China
全文: PDF (505 KB)   HTML  
输出: BibTeX | EndNote (RIS)      
摘要 

目的】解决随着用户数目剧增而造成的协同过滤算法效率过低的问题。【方法】提出一种基于用户分类的协同过滤方法。该方法引入基于规则的分类方法对庞大的用户群分类, 在保证一定的推荐准确度前提下, 为用户寻找局部近邻用户, 并以局部近邻用户基准完成个性化推荐。【结果】分别通过F1与平均绝对误差两个指标进行用户分类与推荐精度评估, 在用户分类准确及推荐精度良好的前提下, 用时间复杂度衡量算法效率。实验结果表明, 引入用户分类的协同过滤推荐效率明显提高。【局限】牺牲一定程度的推荐精度; 仅在MovieLens公开数据集上进行实验测试, 还需在其他数据集上进一步检验。【结论】本文方法可以减少近邻用户识别的计算量, 同时提高算法效率。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
祝婷
秦春秀
李祖海
关键词 个性化推荐协同过滤用户分类规则    
Abstract

[Objective] To solve the problem of low efficiency of the algorithm with the increasing number of users. [Methods] This paper proposes a method of collaborative filtering based on user classification. Firstly, the huge users are classified into several groups according to a rule-based classification method. Then, with the guarantee of recommendation accuracy, the local neighbor users are discovered for users. Finally, based on the discovered local neighbors, personalized recommendation is conducted. [Results] User classification and recommendation accuracy are evaluated by F1 and MAE separately. The algorithm efficiency is evaluated according to the time complexity. Experimental results show that with the adoption of a rule-based user classification, collaborative filtering algorithm significantly improves with the guarantee of user classification accuracy and recommendation accuracy. [Limitations] The recommendation accuracy is reduced a little bit. The proposed method is only tested on MovieLens data set, and it needs further validation in other data sets. [Conclusions] This method reduces the computation of local neighbors user identification, while improves the efficiency of the algorithm.

Key wordsPersonalized recommendation    Collaborative filtering    User classification    Rule
收稿日期: 2014-12-31      出版日期: 2015-07-08
:  G350  
基金资助:

本文系国家自然科学基金项目“基于知识地图的对等网语义社区及其知识共享研究”(项目编号:71103138)和中央高校基本科研业务费专项资金资助项目“大数据背景下基于用户生成内容的商务智能模型研究”(项目编号: BDY231414)的研究成果之一。

通讯作者: 秦春秀, ORCID: 0000-0002-7809-4145, E-mail: cxqin@xidian.edu.cn。     E-mail: cxqin@xidian.edu.cn
作者简介: 作者贡献声明: 祝婷: 提出研究思路, 设计研究方案, 采集、清洗和分析数据; 李祖海: 进行实验; 祝婷, 秦春秀: 论文起草及最终版本修订。
引用本文:   
祝婷, 秦春秀, 李祖海. 基于用户分类的协同过滤个性化推荐方法研究[J]. 现代图书情报技术, 2015, 31(6): 13-19.
Zhu Ting, Qin Chunxiu, Li Zuhai. Research on Collaborative Filtering Personalized Recommendation Method Based on User Classification. New Technology of Library and Information Service, 2015, 31(6): 13-19.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2015.06.03      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2015/V31/I6/13

[1] 中国互联网络信息中心.第34次中国互联网络发展状况统计报告[EB/OL]. [2014-07-21]. http://www.cnnic.net.cn. (China Internet Network Information Center. The 34th Statistical Report on Internet Development in China [EB/OL]. [2014-07-21]. http://www.cnnic.net.cn.)
[2] 安悦, 李兵, 杨瑞泰, 等. 基于内容的热门微话题个性化推荐研究 [J]. 情报杂志, 2014, 33(2): 155-160. (An Yue, Li Bing, Yang Ruitai, et al. Content-based Personalized Recommendation on Popular Micro-topic [J]. Journal of Intelligence, 2014, 33(2): 155-160.)
[3] 索琪, 卢涛. 基于关联规则的电子商务推荐系统研究[J]. 哈尔滨师范大学自然科学学报, 2005, 21(2): 50-53. (Suo Qi, Lu Tao. Research on Recommender System Based on Association Rules [J]. Natural Sciences Journal of Harbin Normal University, 2005, 21(2): 50-53.)
[4] 范波, 程久军. 用户间多相似度协同过滤推荐算法[J]. 计算机科学, 2012, 39(1): 23-26. (Fan Bo, Cheng Jiujun. Collaborative Filtering Recommendation Algorithm Based on User's Multi-similarity [J]. Computer Science, 2012, 39(1): 23-26.)
[5] 王玉斌, 孟祥武, 胡勋. 一种基于信息老化的协同过滤推荐算法[J]. 电子与信息学报, 2013, 35(10): 2391-2396. (Wang Yubin, Meng Xiangwu, Hu Xun. Information Aging- based Collaborative Filtering Recommendation Algorithm [J]. Journal of Electronics & Information Technology, 2013, 35(10): 2391-2396.)
[6] Massa P, Avesani P. Trust-aware Collaborative Filtering for Recommender Systems [C]. In: Proceedings of the 2004 International Conference on Cooperative Information Systems, Agia Napa, Cyprus. 2004.
[7] Sarwar B M, Karypis G, Konstan J, et al. Application of Dimensionality Reduction in Recommender System—A Case Study [C]. In: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Workshop on Web Mining for E-Commerce— Challenges and Opportunities (WEBKDD'00). 2000.
[8] 李玲俐. 数据挖掘中分类算法综述 [J]. 重庆师范大学学报: 自然科学版, 2011, 28(4): 44-47. (Li Lingli. A Review on Classification Algorithms in Data Mining [J]. Journal of Chongqing Normal University: Natural Science, 2011, 28(4): 44-47.)
[9] Agrawal R, Srikant R. Fast Algorithms for Mining Association Rules [C]. In: Proceedings of the 20th International Conference on Very Large Data Bases. 1994.
[10] 杨丽娜, 刘科成, 颜志军. 虚拟研究社区中的知识分享与个性化知识推荐[J]. 中国电化教育, 2010(6): 108-112. (Yang Li'na, Liu Kecheng, Yan Zhijun. Knowledge Sharing and Personalized Knowledge Recommendation in Virtual Research Community [J]. CET China Educational Technology, 2010(6): 108-112.)
[11] Adomavicius G, Tuzhilin A. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-art and Possible Extensions [J]. IEEE Transaction on Knowledge and Data Engineering, 2005, 17(6): 734-749.
[12] Sarwar B, Karypis G, Konstan J, et al. Item-based Collaborative Filtering Recommendation Algorithms [C]. In: Proceedings of the 10th International Conference on World Wide Web. ACM, 2001: 285-295.
[13] Sarwar B, Karypis G, Konstan J, et al. Analysis of Recommendation Algorithms for E-commerce [C]. In: Proceedings of the 2nd ACM Conference on Electronic Commerce. ACM, 2000: 158-167.
[14] Pazzani M, Billsus D. Learning and Revising User Profiles: The Identification of Interesting Web Sites [J]. Machine Learning, 1997, 27(3): 313-331.
[15] 夏培勇. 个性化推荐技术中的协同过滤算法研究[D]. 青岛: 中国海洋大学, 2011. (Xia Peiyong. Research on Collaborative Filtering Algorithm of Personalized Recommendation Technology [D]. Qingdao: China Ocean University, 2011.)
[16] 郭艳红. 推荐系统的协同过滤算法与应用研究 [D]. 大连: 大连理工大学, 2008. (Guo Yanhong. On Collaborative Filtering Algorithm and Applications of Recommender Systems [D]. Dalian: Dalian University of Technology, 2008.)

[1] 吴彦文, 蔡秋亭, 刘智, 邓云泽. 融合多源数据和场景相似度计算的数字资源推荐研究*[J]. 数据分析与知识发现, 2021, 5(11): 114-123.
[2] 李振宇, 李树青. 嵌入隐式相似群的深度协同过滤算法*[J]. 数据分析与知识发现, 2021, 5(11): 124-134.
[3] 丁浩, 艾文华, 胡广伟, 李树青, 索炜. 融合用户兴趣波动时序的个性化推荐模型*[J]. 数据分析与知识发现, 2021, 5(11): 45-58.
[4] 杨辰, 陈晓虹, 王楚涵, 刘婷婷. 基于用户细粒度属性偏好聚类的推荐策略*[J]. 数据分析与知识发现, 2021, 5(10): 94-102.
[5] 杨恒,王思丽,祝忠明,刘巍,王楠. 基于并行协同过滤算法的领域知识推荐模型研究*[J]. 数据分析与知识发现, 2020, 4(6): 15-21.
[6] 苏庆,陈思兆,吴伟民,李小妹,黄佃宽. 基于学习情况协同过滤算法的个性化学习推荐模型研究*[J]. 数据分析与知识发现, 2020, 4(5): 105-117.
[7] 郑淞尹,谈国新,史中超. 基于分段用户群与时间上下文的旅游景点推荐模型研究*[J]. 数据分析与知识发现, 2020, 4(5): 92-104.
[8] 李铁军,颜端武,杨雄飞. 基于情感加权关联规则的微博推荐研究*[J]. 数据分析与知识发现, 2020, 4(4): 27-33.
[9] 魏伟,郭崇慧,邢小宇. 基于语义关联规则的试题知识点标注及试题推荐*[J]. 数据分析与知识发现, 2020, 4(2/3): 182-191.
[10] 丁勇,陈夕,蒋翠清,王钊. 一种融合网络表示学习与XGBoost的评分预测模型*[J]. 数据分析与知识发现, 2020, 4(11): 52-62.
[11] 黄名选,卢守东,徐辉. 基于加权关联模式挖掘与规则后件扩展的跨语言信息检索 *[J]. 数据分析与知识发现, 2019, 3(9): 77-87.
[12] 李纲,周华阳,毛进,陈思菁. 基于机器学习的社交媒体用户分类研究 *[J]. 数据分析与知识发现, 2019, 3(8): 1-9.
[13] 焦富森,李树青. 基于物品质量和用户评分修正的协同过滤推荐算法 *[J]. 数据分析与知识发现, 2019, 3(8): 62-67.
[14] 李珊,姚叶慧,厉浩,刘洁,嘎玛白姆. 基于ISA联合聚类的组推荐算法研究 *[J]. 数据分析与知识发现, 2019, 3(8): 77-87.
[15] 强韶华,罗云鹿,李玉鹏,吴鹏. 基于RBR和CBR的金融事件本体推理研究 *[J]. 数据分析与知识发现, 2019, 3(8): 94-104.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 2015 《数据分析与知识发现》编辑部
地址:北京市海淀区中关村北四环西路33号 邮编:100190
电话/传真:(010)82626611-6626,82624938
E-mail:jishu@mail.las.ac.cn