Please wait a minute...
New Technology of Library and Information Service  2012, Vol. 28 Issue (4): 48-53    DOI: 10.11925/infotech.1003-3513.2012.04.08
Current Issue | Archive | Adv Search |
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
Export: BibTeX | EndNote (RIS)      
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     
Received: 23 February 2012      Published: 20 May 2012



Cite this article:

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.

URL:     OR

[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].

[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] Wu Yanwen, Cai Qiuting, Liu Zhi, Deng Yunze. Digital Resource Recommendation Based on Multi-Source Data and Scene Similarity Calculation[J]. 数据分析与知识发现, 2021, 5(11): 114-123.
[2] Ding Hao, Ai Wenhua, Hu Guangwei, Li Shuqing, Suo Wei. A Personalized Recommendation Model with Time Series Fluctuation of User Interest[J]. 数据分析与知识发现, 2021, 5(11): 45-58.
[3] Yiwen Zhang,Chenkun Zhang,Anju Yang,Chengrui Ji,Lihua Yue. A Conditional Walk Quadripartite Graph Based Personalized Recommendation Algorithm[J]. 数据分析与知识发现, 2019, 3(4): 117-125.
[4] Jiaxin Ye,Huixiang Xiong. Recommending Personalized Contents from Cross-Domain Resources Based on Tags[J]. 数据分析与知识发现, 2019, 3(2): 21-32.
[5] Hao Ding,Shuqing Li. Personalized Recommendation Based on Predictive Analysis of User’s Interests[J]. 数据分析与知识发现, 2019, 3(11): 43-51.
[6] Li Jie,Yang Fang,Xu Chenxi. A Personalized Recommendation Algorithm with Temporal Dynamics and Sequential Patterns[J]. 数据分析与知识发现, 2018, 2(7): 72-80.
[7] Chen Meimei,Xue Kangjie. Personalized Recommendation Algorithm of Multi-faceted Trust Tensor Based on Tag Clustering[J]. 数据分析与知识发现, 2017, 1(5): 94-101.
[8] Chen Meimei,Xue Kangjie. Personalized Recommendation Algorithm Based on Modified Tensor Decomposition Model[J]. 数据分析与知识发现, 2017, 1(3): 38-45.
[9] Tan Xueqing,Zhang Lei,Huang Cuicui,Luo Lin. A Collaborative Filtering and Recommendation Algorithm Using Trust of Domain-Experts and Similarity[J]. 现代图书情报技术, 2016, 32(7-8): 101-109.
[10] Xie Qi,Cui Mengtian. Group Similarity Based Hybrid Web Service Recommendation Algorithm[J]. 现代图书情报技术, 2016, 32(6): 80-87.
[11] Zhu Ting, Qin Chunxiu, Li Zuhai. Research on Collaborative Filtering Personalized Recommendation Method Based on User Classification[J]. 现代图书情报技术, 2015, 31(6): 13-19.
[12] Gao Huming, Zhao Fengyue. A Hybrid Recommendation Method Combining Collaborative Filtering and Content Filtering[J]. 现代图书情报技术, 2015, 31(6): 20-26.
[13] Lu Xiaoming. Research on a Lightweight Academic Library Context-aware Recommendation Service Platform Based on GimbalTM[J]. 现代图书情报技术, 2015, 31(3): 101-107.
[14] Song Meiqing. Research on Multi-granularity Users' Preference Mining Based on Collaborative Filtering Personalized Recommendation[J]. 现代图书情报技术, 2015, 31(12): 28-33.
[15] Wang Weijun, Song Meiqing. A Collaborative Filtering Personalized Recommendation Algorithm Through Directionally Mining Users’ Preferences[J]. 现代图书情报技术, 2014, 30(6): 25-32.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938