Please wait a minute...
New Technology of Library and Information Service  2012, Vol. 28 Issue (3): 8-14    DOI: 10.11925/infotech.1003-3513.2012.03.02
Current Issue | Archive | Adv Search |
Research on Random Walk with Restart Recommendation Algorithm of Explicit Rating
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 Colleage, Nanjing 210088, China;
3. Information Science Department, Pennsylvania State University, Malvern 19355, USA
Download:
Export: BibTeX | EndNote (RIS)      
Abstract  Aiming at random walk with restart recommendation algorithm mainly for implicit ratings while ignoring explicit ratings, this paper sets random walk under supervision to make recommendation, that makes the probabilities of items which user likes are greater than those of items which user dislikes. Experiment result demonstrates that this algorithm improves the accuracy of recommendation.
Key wordsExplicit rating      Random walk with restart      Personalized recommendation     
Received: 03 February 2012      Published: 19 April 2012
: 

TP393

 

Cite this article:

Yu Yan, Qiu Guanghua. Research on Random Walk with Restart Recommendation Algorithm of Explicit Rating. New Technology of Library and Information Service, 2012, 28(3): 8-14.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2012.03.02     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2012/V28/I3/8

[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. Random-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] Yen L, Saerens M, Mantrach A, et al. A Family of Dissimilarity Measures Between Nodes Generalizing both Shortest-path and the Commute-time Distances[C].In:Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD),New York. USA:ACM, 2008: 785-793.

[4] Gori M, Pucci A. A Random-Walk Based Scoring Algorithm with Application to Recommender Systems for Large-Scale E-Commerce[C].In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia. USA:ACM, 2006:127-146.

[5] Pan J Y, Yang H J, Faloutsos C, et al. Automatic Mulitmedia Cross-modal Correlation Discovery[C]. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle. USA: ACM, 2004:653-658.

[6] Fouss F, Pirotte A, Saerens M. A Novel Way of Computing Similarities Between Nodes of a Graph, with Application to Collaborative Recommendation [C].In:Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence. USA: IEEE CPS, 2005: 550-556.

[7] Das A, Datar M, Garg A,et al. Google News Personalization: Scalable Online Collaborative Filtering[C]. In:Proceedings of the 16th International Conference on World Wide Web,Canada.ACM, 2007: 272-280.

[8] Linden G, Smith B, York J. Amazon.com Recommendation: Item-to-item Collaborative Filtering[J]. IEEE Internet Computing, 2003, 7(1): 76-80.

[9] Su X Y, Khoshgoftaar T M. A Survey of Collaborative Filtering Techniques[J]. Advances in Artificial Intelligence, 2009:4.

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

[11] Pan J Y, Yang H J, Faloutsos C, et al. GCap: Graph-based Automatic Image Captioning [C].In: Proceedings of the Conference on Computer Vision and Pattern Recognition Workshop(CVPRW’04). USA:IEEE CPS, 2004: 146.

[12] Urban J, Jose J M. Adaptive Image Retrieval Using a Graph Model for Semantic Feature Integration[C].In:Proceedings of the 8th ACM International Workshop on Multimedia Information Retrieval. USA: ACM, 2006: 117-126.

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

[14] Backstrom L, Leskovec J.Supervised Random Walks:Predicting and Recommending Links in Social Networks [C].In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining. USA: ACM, 2011: 635-644.

[15] Konstas I, Stahopoulos 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,Boston. USA: ACM, 2009: 195-202.

[16] Mobasher B, Dai H H, Luo T, et al. Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization[J]. Data Mining and Knowledge Discovery, 2002, 6(1): 61-82.

[17] Jin X, Zhou Y, Mobasher B.Web Usage Mining Based on Probabilistic Latent Semantic Analysis[C].In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. USA: ACM, 2004: 197-205.

[18] Shao J, Yao L, Cai R Y, et al. Lasso–Based Tag Expansion and Tag–Boosted Collaborative Filtering[C]. In: Proceedings of the Advances in Multimedia Information Processing and the 11th Pacific Rim Conference on Multimedia: Part II. New York: Springer-Verlag,2011,6298: 559-570.

[19] Jin R, Si L, Zhai C X, et al. Collaborative Filtering with Decoupled Models for Preferences and Ratings[C].In: Proceedings of the 12th International Conference on Information and Knowledge Management. USA: ACM, 2003: 309-316.

[20] De K J, Liekens A, Goethals B. GauSo: Graph Base Music Recommendation in a Social Bookmarking Service[C]. In: Proceedings of the 10th International Symposium. New York: Spring-Verlag Inc, 2011:138-149.
[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   E-mail:jishu@mail.las.ac.cn