Computer Science ›› 2018, Vol. 45 ›› Issue (10): 37-42.doi: 10.11896/j.issn.1002-137X.2018.10.007

• CGCKD 2018 • Previous Articles     Next Articles

Recommendation Algorithm Combining User’s Asymmetric Trust Relationships

ZHANG Zi-yin, ZHANG Heng-ru, XU Yuan-yuan, QIN Qin   

  1. School of Computer Science,Southwest Petroleum University,Chengdu 610500,China
  • Received:2018-04-16 Online:2018-11-05 Published:2018-11-05

Abstract: Data sparsity is one of the major challenges faced by collaborative filtering.Trust relationships between users provide useful additional information for the recommender system.In the existing studies,the direct trust relationships are mainly used as additional information,while the indirect trust relationships are less considered.This paper proposed a recommendation algorithm(ATRec) that combines the direct and indirect asymmetric trust relationships.First,a trust transfer mechanism is constructed and used to obtain asymmetric indirect trust relationships between users.Second,each user’s trust set is obtained by the direct and indirect asymmetric trust relationship.At last,the popularity of the item is computed according to the rating information of the trust set or the k-nearest neighbors and the favorable thre- shold,thus generating user’s top-N recommendation list by the recommended threshold.The experimental results show that this algorithm has better performance than the state-of-the-art recommendation algorithms in top-N recommendation.

Key words: Asymmetric trust relationship, Recommender system, Personalized recommendation, Trust transfer mechanism

CLC Number: 

  • TP181
[1]MASSA P,AVESANI P.Trust-aware recommender systems [C]∥2007 ACM Conference on Recommender Systems.ACM,2007:17-24.
[2]ZHAO W X,LI S,HE Y,et al.Connecting social media to e-commerce:Cold-start product recommendation using microblogging information[J].IEEE Transactions on Knowledge and Data Engineering,2016,28(5):1147-1159.
[3]ZHOU J,TANG M,TIAN Y,et al.Social network and tag sources based augmenting collaborative recommender system[J].IEICE Transactions on Information and Systems,2015,98(4):902-910.
[4]ZHANG H R,MIN F.Three-way recommender systems based on random forests[J].Knowledge-Based Systems,2016,91(C):275-286.
[5]O’DONOVAN J,SMYTH B.Trust in r-commender systems [C]∥10th International Conference on Intelligent User Interfaces.ACM,2005:167-174.
[6]DENG A L,ZHU Y Y,SHI B L.A collaborative filtering re- commendation algorithm based on item rating prediction[J].Journal of Software,2003,14(9):1621-1628.(in Chinese)
[7]SINHA R R,SWEARINGEN K.Comparing recommendations made by online systems and friends [C]∥DELOS-NSF Workshop on Personalization and Recommender Systems in Digital Libraries.2001.
[8]MASSA P,BHATTACHARJEE B.Using trust in recommender systems:an experimental analysis[C]∥International Conference on Trust Management.Berlin:Springer,2004:221-235.
[9]MA H,ZHOU D,LIU C,et al.Recommender systems with social regularization[C]∥Forth International Conference on Web Search & Web Data Mining.DBLP,2011:287-296.
[10]HERLOCKER J L,KONSTAN J A,RIEDL J.Explaining collaborative filtering recommendations[C]∥2000 ACMConfe-rence on Computer Supported Cooperative Work.ACM,2000:241-250.
[11]PARK C,KIM D,OH J,et al.Improving top-K recommendation with truster and trustee relationship in user trust network[J].Information Sciences An International Journal,2016,374(C):100-114.
[12]PAN R,ZHOU Y,CAO B,et al.One-class collaborative filtering[C]∥Eighth IEEE International Conference on Data Mining,2008(ICDM’08).IEEE,2008:502-511.
[13]RENDLE S,FREUDENTHALER C,GANTNER Z,et al.BPR:Bayesian personalized ranking from implicit feedback[C]∥Twenty-fifth Conference on Uncertainty in Artificial Intelligence.AUAI Press,2009:452-461.
[14]SHI Y,LARSON M,HANJALIC A.List-wise learning to rank with matrix factorization for collaborative filtering[C]∥Fourth ACM Conference on Recommender Systems.ACM,2010:269-272.
[15]ZHAO T,MCAULEY J,KING I.Leveraging social connections to improve personalized ranking for collaborative filtering[C]∥23rd ACM International Conference on Information and Know-ledge Management.ACM,2014:261-270.
[16]YAO W,HE J,HUANG G,et al.SoRank:incorporating social information into learning to rank models for recommendation[C]∥23rd International Conference on World Wide Web.ACM,2014:409-410.
[1] ZHANG Yan-hong, ZHANG Chun-guang, ZHOU Xiang-zhen, WANG Yi-ou. Diverse Video Recommender Algorithm Based on Multi-property Fuzzy Aggregate of Items [J]. Computer Science, 2019, 46(8): 78-83.
[2] CHEN Jun-hang, XU Xiao-ping, YANG Heng-hong. Research on Recommendation Application Based on Seq2seq Model [J]. Computer Science, 2019, 46(6A): 493-496.
[3] LI Jian-jun, HOU Yue, YANG Yu. User Interest Recommendation Model Based on Context Awareness [J]. Computer Science, 2019, 46(6A): 502-506.
[4] HE Jin-lin, LIU Xue-jun, XU Xin-yan, MAO Yu-jia. Implicit Feedback Recommendation Model Combining Node2vec and Deep Neural Networks [J]. Computer Science, 2019, 46(6): 41-48.
[5] ZHANG Hong-li, BAI Xiang-yu, LI Gai-mei. Personalized Recommendation Algorithm Based on Recent Neighborhood Recommendation and Combined with Context Awareness [J]. Computer Science, 2019, 46(4): 235-240.
[6] SHI Jin-ping,LI Jin,HE Feng-zhen. Diversity Recommendation Approach Based on Social Relationship and User Preference [J]. Computer Science, 2018, 45(6A): 423-427.
[7] LI Hao-yang and FU Yun-qing. Collaborative Filtering Recommendation Algorithm Based on Tag Clustering and Item Topic [J]. Computer Science, 2018, 45(4): 247-251.
[8] WANG Rong-bing, AN Wei-kai, FENG Yong and XU Hong-yan. Important Micro-blog User Recommendation Algorithm Based on Label and PageRank [J]. Computer Science, 2018, 45(2): 276-279.
[9] ZHANG Li-gang and ZHANG Jiu-long. Personalized Affective Video Content Analysis:A Review [J]. Computer Science, 2018, 45(1): 24-28.
[10] CHENG Ying-chao, WANG Rui-hu and HU Zhang-ping. Novel Approach on Collaborative Filtering Based on Gaussian Mixture Model [J]. Computer Science, 2017, 44(Z6): 451-454.
[11] HE Ming, SUN Wang, XIAO Run and LIU Wei-shi. Collaborative Filtering Recommendation Algorithm Combining Clustering and User Preferences [J]. Computer Science, 2017, 44(Z11): 391-396.
[12] ZENG An, GAO Cheng-si and XU Xiao-qiang. Collaborative Filtering Algorithm Incorporating Time Factor and User Preference Properties [J]. Computer Science, 2017, 44(9): 243-249.
[13] WANG Li-e, XU Yuan-xin, LI Xian-xian and LIU Peng. P2P-based Privacy Protection Strategy in Mobile-commerce Recommender System [J]. Computer Science, 2017, 44(9): 178-183.
[14] LI Xiao-lun and DING Zhi-jun. Group Travel Trip Recommendation Method in LBSNs [J]. Computer Science, 2017, 44(6): 199-205.
[15] ZENG An and XU Xiao-qiang. Using Social Trust Relationship and Helpfullness Ratings for Recommendation Based on Matrix Factorization [J]. Computer Science, 2017, 44(4): 288-294.
Full text



[1] . [J]. Computer Science, 2018, 1(1): 1 .
[2] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[3] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[4] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[5] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[6] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[7] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[8] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[9] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[10] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .