Please wait a minute...
Advanced Search
现代图书情报技术  2012, Vol. 28 Issue (4): 48-53     https://doi.org/10.11925/infotech.1003-3513.2012.04.08
  知识组织与知识管理 本期目录 | 过刊浏览 | 高级检索 |
用户兴趣变化感知的重启动随机游走推荐算法研究
俞琰1,2, 邱广华1,3
1. 南京航空航天大学经济管理学院 南京 210016;
2. 东南大学成贤学院计算机系 南京 210088;
3. 美国宾州州立大学信息科学系 马尔文 19355
Research on User Interest Shift Aware Random Walk with Restart Recommendation Algorithm
Yu Yan1,2, Qiu Guanghua1,3
1. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
2. Computer Science Department, Southeast University Chenxian College, Nanjing 210088, China;
3. Information Science Department, Pennsylvania State University, Malvern 19355, USA
全文: PDF (525 KB)   HTML  
输出: BibTeX | EndNote (RIS)      
摘要 针对目前重启动随机游走推荐算法忽略用户兴趣变化的问题,提出一种基于用户兴趣变化的重启动随机游走推荐算法。通过聚类识别用户的兴趣,建立用户兴趣模型,在此基础上,考虑兴趣的时间衰减,计算用户当前兴趣度。最后,根据用户当前兴趣度,形成用户转移概率矩阵,并做出推荐。实验表明提出的算法较传统的重启动随机游走推荐算法可以有效地提高推荐精度。
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
俞琰
邱广华
关键词 兴趣变化重启动随机游走个性化推荐    
Abstract:Aiming at random walk with restart recommendation algorithm ignoring user interest shift, this paper propses a new random walk with restart recommendation algorithm based on user interest shift. It identifies user interest by clustering, then creates user interest model on which estimates user's current interest concerning time decay. Finally, it forms the transition probability to make recommendation according to user current interest. Experiment shows that proposed algorithm can improve the recommendation accuracy efficiently.
Key wordsInterest shift    Random walk with restart    Personalized recommendation
收稿日期: 2012-02-23      出版日期: 2012-05-20
: 

TP393

 
引用本文:   
俞琰, 邱广华. 用户兴趣变化感知的重启动随机游走推荐算法研究[J]. 现代图书情报技术, 2012, 28(4): 48-53.
Yu Yan, Qiu Guanghua. Research on User Interest Shift Aware Random Walk with Restart Recommendation Algorithm. New Technology of Library and Information Service, 2012, 28(4): 48-53.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2012.04.08      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2012/V28/I4/48
[1] Wang Z Q, Tan Y W, Zhang M. Graph-based Recommendation on Social Networks[C]. In: Proceedings of the 12th International Asia-Pacific Web Conference(APWEB), Busan. 2010:116-122.

[2] Fouss F, Pirotte A, Renders J M, et al. Radnom-walk Computation of Similarities Between Nodes of a Graph with Application to Collaborative Recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2007,19(3):355-369.

[3] Yildirim H, Krishnamoorthy M S. A Random Walk Method for Alleviating the Sparsity Problem in Collaborative Filtering[C]. In: Proceedings of the 2008 ACM Conference on Recommender Systems(RecSys'08). USA:ACM, 2008: 131-138.

[4] Konstas I, Stathopoulos V, Jose J M. On Social Networks and Collaborative Recommendation[C].In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. USA: ACM, 2009: 195-202.

[5] 俞琰,邱广华. 显式评分的重启动随机游走推荐算法研究[J]. 现代图书情报技术, 2012 (3):8-14. (Yu Yan, Qiu Guanghua. Research on Random Walk with Restart Recommendation Algorithm of Explicit Rating[J]. New Technology of Library and Information Service, 2012 (3):8-14.)

[6] Tong H H, Faloutsos C, Pan J Y. Fast Random Walk With Restart and Its Applications[C].In: Proceedings of the 6th International Conference on Data Mining(ICDM'06).USA:IEEE CPS, 2006: 613-622.

[7] Wu P, Yeung C H, Liu W, et al.Time Aware Collaborative Filtering with Piecewise Decay Functionl[J/OL]. [2012-03-01]. http://arxiv.org/abs/1010.3988.

[8] Koren Y. Collaborative Filtering with Temporal Dynamics[J]. Communications of the ACM, 2010,53(4):89-97.

[9] Cao H H, Chen E, Yang J, et al. Enhancing Recommender Systems Under Volatile Userinterest Drifts[C].In: Proceedings of the 18th ACM Conference on Information and Knowledge Mangement. USA: ACM, 2009:1257-1266.

[10] Zhang Y C, Liu Y Z. A Collaborative Filtering Algorithm Based on Time Period Partition[C]. In: Proceeding of the 3rd International Symposium on Intelligent Information Technology and Security Informatics.USA: IEEE, 2010: 777-780.

[11] Chen Z M, Jiang Y, Zhao Y. A Collaborative Filtering Recommendation Algorithm Based on User Interest Change and Trust Evaluation[J]. International Journal of Digital Content Technology and its Applications, 2010,9(4):106-113.

[12] Han X W, Zhao T J. Auto-k Dynamic Clustering Algorithm[J]. Asian Journal of Information Technology,2005,4(4):467-471.

[13] Deshpande M, Karypis G. Item Based Top N Recommendation Algorithms[J]. ACM Transactions on Information Systems, 2004,22(1): 143-177.
[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] 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.
[3] 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.
[4] 张怡文,张臣坤,杨安桔,计成睿,岳丽华. 基于条件型游走的四部图推荐方法*[J]. 数据分析与知识发现, 2019, 3(4): 117-125.
[5] 叶佳鑫,熊回香. 基于标签的跨领域资源个性化推荐研究*[J]. 数据分析与知识发现, 2019, 3(2): 21-32.
[6] 聂卉. 结合词向量和词图算法的用户兴趣建模研究 *[J]. 数据分析与知识发现, 2019, 3(12): 30-40.
[7] 丁浩,李树青. 基于用户多类型兴趣波动趋势预测分析的个性化推荐方法 *[J]. 数据分析与知识发现, 2019, 3(11): 43-51.
[8] 李杰, 杨芳, 徐晨曦. 考虑时间动态性和序列模式的个性化推荐算法*[J]. 数据分析与知识发现, 2018, 2(7): 72-80.
[9] 侯银秀, 李伟卿, 王伟军, 张婷婷. 基于用户偏好与商品属性情感匹配的图书个性化推荐研究*[J]. 数据分析与知识发现, 2017, 1(8): 9-17.
[10] 陈梅梅, 薛康杰. 基于标签簇多构面信任关系的个性化推荐算法研究*[J]. 数据分析与知识发现, 2017, 1(5): 94-101.
[11] 陈梅梅, 薛康杰. 基于改进张量分解模型的个性化推荐算法研究*[J]. 数据分析与知识发现, 2017, 1(3): 38-45.
[12] 谭学清,张磊,黄翠翠,罗琳. 融合领域专家信任与相似度的协同过滤推荐算法研究*[J]. 现代图书情报技术, 2016, 32(7-8): 101-109.
[13] 谢琪,崔梦天. 基于相似性群体的混合型Web服务推荐*[J]. 现代图书情报技术, 2016, 32(6): 80-87.
[14] 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.
[15] 高虎明, 赵凤跃. 一种融合协同过滤和内容过滤的混合推荐方法[J]. 现代图书情报技术, 2015, 31(6): 20-26.
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