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New Technology of Library and Information Service  2014, Vol. 30 Issue (11): 10-16    DOI: 10.11925/infotech.1003-3513.2014.11.02
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A Review of Research on Trust Recommendation in Social Networks
Tan Xueqing, Huang Cuicui, Luo Lin
School of Information Management, Wuhan University, Wuhan 430072, China
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Abstract  

[Objective] Discuss the role of social networks to solve problems such as data sparseness and cold start of traditional personalized recommendation systems. [Coverage] This paper retrieves research literatures about trust recommendation at home and abroad from Springer and Google Scholar since 2004. [Methods] It summarizes the related literatures from perspectives of trust and distrust. [Results] Based on the summary, this paper demonstrates the existing problems such as the deficiency of calculation method for trust and lack of in-depth study of distrust and so on. [Limitations] Other factors in social networks should be combined with trust in an in-depth comparative analysis. [Conclusions] Context-aware trust recommendation, mining the value of weak relationship in social networks can be new valuable research directions in future.

Key wordsPersonalized recommendation      Social networks      Trust recommendation     
Received: 12 May 2014      Published: 18 December 2014
:  G354  

Cite this article:

Tan Xueqing, Huang Cuicui, Luo Lin. A Review of Research on Trust Recommendation in Social Networks. New Technology of Library and Information Service, 2014, 30(11): 10-16.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2014.11.02     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2014/V30/I11/10

[1] Jannach D, Zanker M, Felferning A, et al. Recommender Systems: An Introduction [M]. Cambridge University Press, 2010.
[2] Sinha R R, Swearingen K. Comparing Recommendations Made by Online Systems and Friends [C]. In: Proceedings of the 2nd DELOS Network of Excellence Workshop on Personalisation and Recommender Systems in Digital Libraries, Dublin City University, Ireland. 2001.
[3] McKnight H, Chervany N. The Meaning of Trust [R]. Technical Report MISRC 96-04, Management Information Systems Research Center, University of Minnesota, USA, 1996.
[4] 方曙光. "弱关系"和"强关系"下的网络互动和网络运动[J]. 北京理工大学学报: 社会科学版, 2014, 16(2): 135-141. (Fang Shuguang. Network Interaction and Movement from the Perspective of "Weak Ties" and "Strong Ties"[J]. Journal of Beijing Institute of Technology: Social Sciences Edition, 2014, 16(2): 135-141. )
[5] Mui L, Mohtashemi M, Halberstadt A. A Computational Model of Trust and Reputation for E-businesses [C]. In: Proceedings of the 35th Annual Hawaii International Conference on System Sciences. 2002: 188-196.
[6] Weng J, Miao C, Goh A. Improving Collaborative Filtering with Trust-based Metrics [C]. In: Proceedings of the 2006 ACM Symposium on Applied Computing. 2006: 1860-1864.
[7] Massa P, Avesani P. Trust Metrics in Recommender Systems[A]//GolbeckJ. Computing with Social Trust Human- Computer Interaction Series 2009 [M]. Springer London, 2009: 259-285.
[8] 傅敏. 基于信任和不信任的协同过滤推荐模型研究[D]. 秦皇岛: 燕山大学, 2012. (Fu Min. A Research on Trust and Distrust-based Collaborative Filtering Recommendation Model [D]. Qinhuangdao: Yanshan University, 2012.)
[9] Avesani P, Massa P, Tiella R. A Trust-enhanced Recommender System Application: Moleskiing [C]. In: Proceedings of the 2005 ACM Symposium on Applied Computing, Santa Fe, New Mexico, USA. 2005: 1589-1593.
[10] Zhang Y, Wu Z H, Chen H J, et al. Mining Target Marketing Groups from Users' Web of Trust on Epinions [C]. In: Proceedings of the 2008 AAAI Spring Symposium. 2008: 116-121.
[11] Golbeck J. Generating Predictive Movie Recommendations from Trust in Social Networks [C]. In: Proceedings of the 4th International Conference on Trust Management (iTrust'06), Pisa, Italy. Springer-Verlag Berlin, Heidelberg, 2006: 93-104.
[12] Golbeck J. Computing and Applying Trust in Web-based Social Networks [D]. University of Maryland, 2005.
[13] Massa P, Avesani P. Trust Metrics on Controversial Users: Balancing Between Tyranny of the Majority and Echo Chambers [J]. International Journal on Semantic Web and Information Systems, 2007, 3(1): 39-64.
[14] Papagelis M, Plexousakis D, Kutsuras T. Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences [C]. In: Proceedings of the 3rd International Conference on Trust Management (iTrust'05). Springer Berlin Heidelberg, 2005: 224-239.
[15] Jøsang A, Marsh S, Pope S. Exploring Different Types of Trust Propagation [C]. In: Proceedings of the 4th International Conference (iTrust'06), Pisa, Italy. Springer Berlin Heidelberg, 2006: 179-192.
[16] 曾赛. 基于社交网络信任模型的商品推荐系统[D]. 广州: 华南理工大学, 2012. (Zeng Sai. Trust Model of Social Network-based Recommational System of Commodity [D]. Guangzhou: South China University of Technology, 2012.)
[17] Zhang B, Huang Z, Yu J, et al. Trust Computation for Multiple Routes Recommendation in Social Network Sites [J]. Security and Communication Networks, 2014. DOI: 10.1002/sec.935.
[18] Yuan W, Shu L, Chao H C, et al. ITARS: Trust-aware Recommender System Using Implicit Trust Networks [J]. IET Communications, 2010, 4(14): 1709-1721.
[19] Martin-Vicente M I, Gil-Solla A, Cabrer M R. Implicit Trust Networks: A Semantic Approach to Improve Collaborative Recommendations[A]//Recommender Systems for the Social Web[M]. Springer, 2012: 107-119.
[20] O'Donovan J, Smyth B. Mining Trust Values from Recom­mendation Errors [J]. International Journal on Artificial Intelligence Tools, 2006, 15(6): 945-962.
[21] O'Donovan J, Smyth B. Trust in Recommender Systems [C]. In: Proceedings of the 10th International Conference on Intelligent User Interfaces, San Diego, California, USA. 2005: 167-174.
[22] Lumbreras A, Gavalda R. Applying Trust Metrics Based on User Interactions to Recommendation in Social Networks [C]. In: Proceedings of the 1st International Workshop of Social Knowledge Discovery and Utilization, Istanbul, Turkey. 2012.
[23] Ziegler C N, Lausen G. Propagation Models for Trust and Distrust in Social Networks [J]. Information Systems Frontiers, 2005, 7(4-5): 337-358.
[24] Ziegler C N, Golbeck J. Investigating Correlations of Trust and Interest Similarity-Do Birds of a Feather Really Flock Together?[J]. Decision Support Systems-DSS, 2007, 43(2): 460-475.
[25] Crandall D, Cosley D, Huttenlocher D, et al. Feedback Effects between Similarity and Social Influence in Online Communities [C]. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2008: 160-168.
[26] Victor P, Cornelis C, De Cock M. Trust Networks for Recommender Systems [M]. Springer, 2011.
[27] Jamali M, Ester M. TrustWalker: A Random Walk Model for Combining Trust-based and Item-based Recommendation [C]. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2007: 397-406.
[28] Burke R. Hybrid Web Recommender Systems[A]//The Adaptive Web [M]. Springer Berlin Heidelberg, 2007: 377-408.
[29] Birtolo C, Ronca D, Aurilio G. Trust-aware Clustering Collaborative Filtering: Identification of Relevant Items[A]//Artificial Intelligence Applications and Innovations [M]. Springer, 2012: 374-384.
[30] Ma H, Lyu M R, King I. Learning to Recommend with Trust and Distrust Relationships[C]. In: Proceedings of the 3rd ACM Conference on Recommender Systems. New York: ACM, 2009: 189-196.
[31] Guha R, Kumar R, Raghavan P, et al. Propagation of Trust and Distrust[C]. In: Proceedings of the 13th International Conference on World Wide Web. 2004: 403-412.
[32] Victor P, Cornelis C, De Cock M, et al. Trust- and Distrust-Based Recommendations for Controversial Reviews [J]. IEEE Intelligent Systems, 2011, 26(1): 48-55.
[33] Yuan J F, Li L, Tan F. Dealing with Trust, Distrust and Ignorance [A]// Knowledge Science, Engineering and Management [M]. Springer Berlin Heidelberg, 2013: 551-560.
[34] 韩丽. 社交网络中的信任推荐和好友搜索过滤算法研究[D]. 秦皇岛: 燕山大学, 2012. (Han Li. Research on Recommendation and Search Filter of Friends Algorithm in Social Networks [D]. Qinhuangdao: Yanshan University, 2012.)
[35] Abbassi Z, Aperjis C, Huberman B A. Friends versus the Crowd: Tradeoffs and Dynamics [R]. HP Report, 2013.
[36] Liu X. Towards Context-Aware Social Recommendation via Trust Networks[A]//Web Information Systems Engineering— WISE2013 [M]. Springer, 2013: 121-134.
[37] Wang Y, Li L, Liu G. Social Context-aware Trust Inference for Trust Enhancement in Social Network Based Recomm­endations on Service Providers [J]. World Wide Web,2013: 1-26.DOI:10.1007/s11280-013-0241-5.
[38] Victor P, Cornelis C, De Cock M, et al. Key Figure Impact in Trust-enhanced Recommender Systems [J]. AI Communica­tions, 2008, 21(2-3): 127-143.
[39] Victor P, Cornelis C, Teredesai A, et al. Whom should I Trust?:The Impact of Key Figures on Cold Start Recommendations [C]. In:Proceedings of the 2008 ACM Symposium on Applied Computing (SAC). 2008: 2014-2018.
[40] 郭磊, 马军, 陈竹敏. 一种信任关系强度敏感的社会化推荐算法[J]. 计算机研究与发展, 2013, 50(9): 1805-1813, 2013. (Guo Lei, Ma Jun, Chen Zhumin. Trust Strength Aware Social Recommendation Method [J]. Journal of Computer Research and Development, 2013, 50(9): 1805-1813, 2013.)
[41] Ginsberg M. Multi-valued Logics: A Uniform Approach to Reasoning in Artificial Intelligence [J]. Computational Intelligence, 1988, 4(3): 265-316.
[42] Liu J, Zhang F, Song X Y, et al. What's in a Name?:An Unsupervised Approach to Link Users Across Communities [C]. In: Proceedings of the 6th ACM International Conference on Web Search and Data Mining. 2013: 495-504.

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