Please wait a minute...
New Technology of Library and Information Service  2014, Vol. 30 Issue (10): 70-75    DOI: 10.11925/infotech.1003-3513.2014.10.11
Current Issue | Archive | Adv Search |
A Recommendation Algorithm Based on Random Walk in Trust Network
Yuan Fuyong, Cai Honglei
College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao 066004, China
Download: PDF(531 KB)   HTML  
Export: BibTeX | EndNote (RIS)      

[Objective] The current recommendation algorithm based on trust confronted with these issues: the explicit trust value is not accurate enough, the implicit trust value is hard to measure, the trust propagation path is not easy to determine. For that this paper presents a recommendation algorithm based on random walk in the trust network. [Methods] The algorithm uses the Bipartite network-based projection structure measure trust value between users, then these values are the formation of the transition probability matrix for random-walk with restart in the users projection network, random walk process does not stop until the trust distribution tends to be steady, namely the trust maximum entropy is achieved. The trust distribution at this time is the final trust matrix. [Results] The experiments on MovieLens dataset show that the improved Bipartite recommendation algorithm by adding user preferences significantly improves the Mean Absolute Error (MAE), Mean Reciprocal Rank(MRR) and normalize Discounted Cumulative Gain (nDCG) compared to other algorithms. [Limitations] Due to the cold start problems in the Bipartite network-based projection algorithm, this algorithm suffers from the new user/new item problem also. [Conclusions] That is to say, this algorithm can make the recommendation more accurate and successfully recommended objects rank in the front of the list, so this algorithm has a strong application value.

Key wordsTrust      Bipartite network-based      Random-walk      Recommendation algorithm     
Received: 25 February 2014      Published: 28 November 2014
:  TP301  

Cite this article:

Yuan Fuyong, Cai Honglei. A Recommendation Algorithm Based on Random Walk in Trust Network. New Technology of Library and Information Service, 2014, 30(10): 70-75.

URL:     OR

[1] Pazzani M J, Billsus D. Content-Based Recommendation Systems [A]//Brusilovsky P, Kobsa A, Nejdl W. The Adaptive Web [M]. Berlin: Springer, 2007: 325-341.
[2] Degemmis M, Lops P, Semeraro G. A Content-Collaborative Recommender that Exploits Wordnet-based User Profiles for Neighborhood Formation [J]. User Modeling and User-Adapted Interaction, 2007, 17(3): 217-255.
[3] Chen Y, Cheng L. A Novel Collaborative Filtering Approach for Recommending Ranked Items [J]. Expert Systems with Applications, 2008, 34(4): 2396-2405.
[4] Konstan J A, Miller B N, Maltz D, et al. GroupLens: Applying Collaborative Filtering to Usenet News [J]. Communications of the ACM, 1997, 40(3): 77-87.
[5] Herlocker J L, Konstan J A, Terveen L G, et al. Evaluating Collaborative Filtering Recommender Systems [J]. ACM Transactions on Information System (TOIS), 2004, 22(1): 5-53.
[6] Guo G. Improving the Performance of Recommender Systems by Alleviating the Data Sparsity and Cold Start Problems[C]. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence. 2013: 3217-3218.
[7] Guo G. Resolving Data Sparsity and Cold Start in Recommender Systems [C]. In: Proceedings of the 20th International Conference on User Modeling, Adaptation and Personalization. 2012: 361-364.
[8] Zhou T, Ren J, Medo M, et al. Bipartite Network Projection and Personal Recommendation [J]. Physical Review E, 2007, 76: Article No. 046155.
[9] Zhang Y C, Blaitner M, Yu Y. Heat Conduction Process on Community Networks as a Recommendation Model [J]. Physical Review Letters, 2007, 99(15): Article No. 154301.
[10] Guo G, Zhang J, Thalmann D, et al. From Ratings to Trust: An Empirical Study of Implicit Trust in Recommender Systems [C]. In: Proceedings of the 29th Annual ACM Symposium on Applied Computing, 2014: 248-253.
[11] Guo G, Erdt M H, Lee B S. A Hybrid Recommender System Based on Material Concepts with Difficulty Levels [C]. In: Proceedings of the 21st International Conference on Computers in Education. 2013.
[12] Morales J M, Peis E, Herrera-Viedma E. A Filtering and Recommender System for E-Scholars [J]. International Journal of Technology of Enhanced Learning, 2010, 2(2): 227-240.
[13] Guo G. Integrating Trust and Similarity to Ameliorate the Data Sparsity and Cold Start for Recommender Systems [C]. In: Proceedings of the 7th ACM Conference on Recommender Systems. ACM, 2013: 451-454.
[14] 刘淇, 陈恩红. 结合二部图投影与排序的协同过滤[J]. 小型微型计算机系统, 2010, 31(5): 835-839. (Liu Qi, Chen Enhong. Collaborative Filtering Through Combining Bipartite Graph Projection and Ranking [J]. Journal of Chinese Computer Systems, 2010, 31(5): 835-839.)
[15] 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.
[16] Moffat A, Zobel J. Rank-based Precision for Measurement of Retrieval Effectiveness [J]. Transactions on Information Systems (TOIS), 2008, 27(1): Article No. 2.

[1] Dong Jing,Dayong Zhang. Assessing Trust-Based Users’ Influence in Social Media[J]. 数据分析与知识发现, 2018, 2(7): 26-33.
[2] Jun Hou,Kui Liu,Qianmu Li. Classification Recommendation Based on ESSVM[J]. 数据分析与知识发现, 2018, 2(3): 9-21.
[3] Liyi Zhang,Huiran Li. Studying Social Interaction of Online Medical Question-Answering Service[J]. 数据分析与知识发现, 2018, 2(1): 76-87.
[4] Fuliang Xue,Junling Liu. Improving Collaborative Filtering Recommendation Based on Trust Relationship Among Users[J]. 数据分析与知识发现, 2017, 1(7): 90-99.
[5] Meimei Chen,Kangjie Xue. 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.
[6] Wang Yong,Deng Jiangzhou,Deng Yongheng,Zhang Pu. A Collaborative Filtering Recommendation Algorithm Based on Item Probability Distribution[J]. 现代图书情报技术, 2016, 32(6): 73-79.
[7] Wu Yingliang, Yao Huaidong, Li Cheng'an. An Improved Collaborative Filtering Recommendation Algorithm with Indirect Trust Relationship[J]. 现代图书情报技术, 2015, 31(9): 38-45.
[8] 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.
[9] Wu Zhenxin, Wang Yuju, Fu Honghu, Li Chunwang, Liu Jianhua. Constructing a Trusted Ingest Workflow of Digital Preservation System[J]. 现代图书情报技术, 2015, 31(3): 1-7.
[10] Li Hui, Xiang Huating, Tang Qiang. A Trust Model for Wikipedia Based on Structure Information and Edit History[J]. 现代图书情报技术, 2015, 31(3): 33-38.
[11] Zhu Hou. Co-evolution of Social Networks and Public Opinion Considering the Effect of Trust and Authority[J]. 现代图书情报技术, 2015, 31(10): 50-57.
[12] 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.
[13] Wu Kun, Xie Xiaqing, Wu Xu. Design and Implementation of Trustworthiness Validation in Cloud Library Virtualized Environment[J]. 现代图书情报技术, 2014, 30(3): 35-41.
[14] Tan Xueqing, Huang Cuicui, Luo Lin. A Review of Research on Trust Recommendation in Social Networks[J]. 现代图书情报技术, 2014, 30(11): 10-16.
[15] Xing Yanyan, Su Jing. Research on the Model of Community Formation Based on Trust in Peer-to-Peer Network[J]. 现代图书情报技术, 2012, 28(6): 43-49.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938