Please wait a minute...
New Technology of Library and Information Service  2015, Vol. 31 Issue (9): 38-45    DOI: 10.11925/infotech.1003-3513.2015.09.06
Current Issue | Archive | Adv Search |
An Improved Collaborative Filtering Recommendation Algorithm with Indirect Trust Relationship
Wu Yingliang, Yao Huaidong, Li Cheng'an
E-Business Department, South China University of Technology, Guangzhou 510006, China
Download: PDF(597 KB)   HTML  
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] In traditional collaborative filtering algorithms, the issues such as data sparsity may make the quality of recommendation worse. This paper attempts to solve it by optimizing the recommendation mechanisms. [Methods] This paper uses cohesive subgroup analysis techniques to identify indirect trust relationship in trust networks, and combines with direct trust relationship to generate an integrated trust, which is used to calculate the user similarity in the new collaborative filtering recommendation algorithm. [Results] Experimental results show that the ultimate trust combining 35% direct and 65% indirect relationship can improve the accuracy of CF algorithms, and compared with only using direct trust relationship, the indirect trust relationship could not be ignored. [Limitations] When considering the indirect trust in the trust network, this paper ignores the impact of more intermediate nodes between two users. [Conclusions] Soft integration of indirect trust relationship can improve the recommendation accuracy of collaborative filtering algorithms.
Received: 04 January 2015      Published: 06 April 2016
:  TP301  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors

Cite this article:

Wu Yingliang, Yao Huaidong, Li Cheng'an. An Improved Collaborative Filtering Recommendation Algorithm with Indirect Trust Relationship. New Technology of Library and Information Service, 2015, 31(9): 38-45.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2015.09.06     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2015/V31/I9/38

[1] Wu Y, Wang X. A Managerial Object-Oriented Solution of Knowledge Management Systems: Based on the Research and Practice of Enterprises of China [J]. Advances in Information Sciences and Service Sciences, 2012, 4(15): 280-288.
[2] 胡吉明. 社会化网络服务的开放运行架构及服务拓展研究[J]. 情报科学, 2012, 30(9): 1396-1400. (Hu Jiming. Study on Open Operation Architecture and Service Expansion of Social Network Service [J]. Information Science in China, 2012, 30(9): 1396-1400.)
[3] Ortega F, Sánchez J L, Bobadilla J, et al. Improving Collaborative Filtering-Based Recommender Systems Results Using PARETO Dominance [J]. Information Sciences, 2012, 239: 50-61.
[4] 冷亚军, 陆青, 梁昌勇. 协同过滤推荐技术综述[J]. 模式识别与人工智能, 2014, 27(8): 720-734. (Leng Yajun, Lu Qing, Liang Changyong. Survey of Recommendation Based on Collaborative Filtering [J]. Pattern Recognition and Artificial Intelligence, 2014, 27(8): 720-734.)
[5] 林聚任. 社会网络分析: 理论、方法与应用[M]. 北京: 北京师范大学出版社, 2009: 80-85. (Lin Juren. Social Network Analysis: Theory, Method and Application [M]. Beijing: Beijing Normal University Press, 2009: 80-85.)
[6] Morales A J, Losada J C, Benito R M. Users Structure and Behavior on an Online Social Network During a Political Protest [J]. Physica A: Statistical Mechanics and Its Applications, 2012, 391(21): 5244-5253.
[7] 张莉, 滕丕强, 秦桃. 利用社会网络关键用户改进协同过滤算法性能[J]. 情报杂志, 2014, 33(4): 196-200. (Zhang Li, Teng Piqiang, Qin Tao. Using Key Users of Social Network to Enhance Collaborative Filtering Performance [J]. Journal of Intelligence, 2014, 33(4): 196-200.)
[8] 俞琰, 邱广华. 基于局部随机游走的在线社交网络朋友推荐算法[J]. 系统工程, 2013, 31(2): 47-54. (Yu Yan, Qiu Guanghua. Algorithm of Friend Recommendation in Online Social Networks Based on Local Random Walk [J]. System Engineering, 2013, 31(2): 47-54.)
[9] 金亚亚, 牟援朝. 基于改进信任度的协同过滤推荐算法[J]. 现代图书情报技术, 2010(10): 49-53. (Jin Yaya, Mou Yuanchao. Collaborative Filtering Recommendation Algorithm Based on Improved Trustworthiness [J]. New Technology of Library and Information Service, 2010(10): 49-53.)
[10] 刘军. 整体网分析讲义UCINET软件实用指南[M]. 上海: 格致出版社, 2009: 117-120. (Liu Jun. Lectures on Whole Network Approach -A Practice Guide to UCINET [M]. Shanghai: Truth & Wisdom Press, 2009: 117-120.)
[11] Zhou T, Ren J, Medo M, et al. Bipartite Network Projection and Personal Recommendation [J]. Physical Review E, 2007, 76(4): 46-115.
[12] Pham M C, Cao Y, Klamma R, et al. A Clustering Approach for Collaborative Filtering Recommendation Using Social Network Analysis [J]. Journal of Universal Computer Science, 2011, 17 (4): 583-604.
[13] Mao L Y, Guan Y, Shang M, et al. Effect of User's Judging Power on the Recommendation Performance [J]. TELKOMNIKA Indonesian Joumal of Electrical Engineering, 2013, 11 (7): 3534-3540.
[14] 董晓华, 周彦晖. 基于相似度的信任推荐模型[J]. 计算机科学, 2013, 40(10): 132-134. (Dong Xiaohua, Zhou Yanhui. Similarity-based Trust Recommended Model [J]. Computer Science, 2013, 40(10): 132-134.)
[15] 甘早斌, 曾灿, 李开, 等. 电子商务下的信任网络构造与优化[J]. 计算机学报, 2012, 35(1): 27-37. (Gan Zaobin, Zeng Can, Li Kai, et al. Construction and Optimization of Trust Network in E-Commerce Environment [J]. Chinese Journal of Computers, 2012, 35(1): 27-37.)
[16] 黄武汉, 孟祥武, 王立才. 移动通信网中基于用户社会化关系挖掘的协同过滤算法[J]. 电子与信息学报, 2011, 33(12): 3002-3007. (Huang Wuhan, Meng Xiangwu, Wang Licai. A Collaborative Filtering Algorithm Based on Users' Social Relationship Mining in Mobile Communication Network [J]. Journal of Electronics & Information Technology, 2011, 33(12): 3002-3007.)
[17] 冯勇, 李军平, 徐红艳, 等. 基于社会网络分析的协同推荐方法改进[J]. 计算机应用, 2013, 33(3): 841-844. (Feng Yong, Li Junping, Xu Hongyan, et al. Collaborative Recommendation Method Improvement Based on Social Network Analysis [J]. Journal of Computer Applications, 2013, 33(3): 841-844.)
[18] Spertus E, Sahami M, Buyukkokten O. Evaluating Similarity Measures: A Large-scale Study in the Orkut Social Network [C]. In: Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. ACM, 2005: 678-684.
[19] Geyer W, Dugan C, Millen D R, et al. Recommending Topics for Self-descriptions in Online User Profiles [C]. In: Proceedings of the 2008 ACM Conference on Recommender Systems. ACM, 2008: 257-271.
[20] Groh G, Ehmig C. Recommendations in Taste Related Domains: Collaborative Filtering VS. Social Filtering [C]. In: Proceedings of the 2007 International ACM Conference on Supporting Group Work. ACM, 2007: 127-136.
[1] Liqing Qiu,Wei Jia,Xin Fan. Influence Maximization Algorithm Based on Overlapping Community[J]. 数据分析与知识发现, 2019, 3(7): 94-102.
[2] Jiang Shuhao, Pan Xuhua, Xue Fuliang. An Independent Recommendation Diversity Optimization Algorithm Based on Item Clustering[J]. 现代图书情报技术, 2015, 31(5): 34-41.
[3] Yuan Fuyong, Cai Honglei. A Recommendation Algorithm Based on Random Walk in Trust Network[J]. 现代图书情报技术, 2014, 30(10): 70-75.
[4] Xue Fuliang, Zhang Huiying. A Research of Collaborative Filtering Recommender Method Based on SOM and RBFN Filling Missing Values[J]. 现代图书情报技术, 2014, 30(7): 56-63.
[5] Jiang Shuhao, Xue Fuliang. An Improved Content-based Recommendation Method Through Collaborative Predictions and Fuzzy Similarity Measures[J]. 现代图书情报技术, 2014, 30(2): 41-47.
[6] Zhang Huiying, Xue Fuliang. An Improved Collaborative Filtering Recommendation Algorithm Based on Vague Sets Theory[J]. 现代图书情报技术, 2012, 28(3): 35-39.
[7] Zhang Huiying, Xue Fuliang. An Integrated Recommender Method Based on CLV and Collaborative Filtering[J]. 现代图书情报技术, 2012, 28(1): 46-52.
[8] Tian Jinfeng, Zeng Xinhong, Huang Huajun, Lin Weiming. Research on Automatic Construction of Definition Notes for Concepts in OntoThesaurus[J]. 现代图书情报技术, 2011, (11): 9-16.
[9] Li Ying Wu Yuanyuan Ning Fujin. Application of a Modified K-means Clustering Algorithm Based on PSO in Customer Segmentation of Securities Industry[J]. 现代图书情报技术, 2010, 26(7/8): 88-94.
[10] Zhang Wei,Yu Yang,You Hongliang. Relation Identification Between Conceptual Terms for Automatic Construction of Lexical Knowledge Database[J]. 现代图书情报技术, 2009, 25(11): 10-16.
[11] Ding Zhenguo,Li Chengjia,Tian Wanxin. A Research of Web Call Center Routing and Queuing Algorithm[J]. 现代图书情报技术, 2009, 25(7-8): 65-69.
[12] Shi Jing,Zhang Lijuan. Extending Inside-outside Algorithm by Using HowNet[J]. 现代图书情报技术, 2009, 25(7-8): 54-58.
[13] Yi Ming,Yang Bin. A Survey of Website Structure Optimization[J]. 现代图书情报技术, 2008, 24(7): 61-65.
[14] Quan Lixin. Web Services Discovery Based on the Fixed-point Semantics[J]. 现代图书情报技术, 2007, 2(6): 29-32.
[15] Hang Yueqin,Yao Ying,Shen Jie . Towards Context Query Information Extraction Based on Single Document[J]. 现代图书情报技术, 2006, 1(10): 30-33.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626，82624938   E-mail:jishu@mail.las.ac.cn