JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2016, Vol. 51 ›› Issue (1): 89-94.doi: 10.6040/j.issn.1671-9352.1.2015.106

Previous Articles     Next Articles

Trust-aware product recommendation based on factorization machine

GAO Xiao-bo1, FANG Xian-mei1,2*, LI Shi-jun2   

  1. 1. School of Computer and Information Engineering, Hechi University, Yizhou 546300, Guangxi, China;
    2. Computer School, Wuhan University, Wuhan 430079, Hubei, China
  • Received:2015-08-06 Online:2016-01-16 Published:2016-11-29

PDF (PC)

1100

Abstract: The personalized recommender system suffers from sparse data and slow recommendation speed. A score prediction model based on Factorization Machine(FM)was proposed. The FM model utilizes users access history, identifies user-interested contents based on their scoring records and integrates trusts among different users. FM has a linear time complexity and excellent learning capability for sparse data, so it can quickly recommend. The results showed that the proposed FM model based on product recommendation approach was significantly more accurate than the traditional methods.

Key words: e-commerce, factorization machine, trust, product recommendation

CLC Number: 

  • TP391
[1] ADOMAVICIUS G, TUZHAILIN A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions[J]. Knowledge and Data Engineering, IEEE Transactions on, 2005, 17(6):734-749.
[2] GOLDBERG D, Nichols D, OKI B M, et al. Using collaborative filtering to weave an information tapestry[J].Communications of the ACM, 1992, 35(12):61-70.
[3] BREESE J S, HECKERMAN D, KADIE C. Empirical analysis of predictive algorithms for collaborative filtering[C] //Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence. Madison, Wisconsin: Morgan Kaufmann Publishers Inc, 1998:43-52.
[4] SARWAR B, KARYPIS G, KONSTAN J, et al. Item-based collaborative filtering recommendation algorithms[C] //Proceedings of the 10th International Conference on World Wide Web. Hong Kong: ACM, 2001: 285-295.
[5] YOSHII K, GOTO M, KOMATANI K, et al. An efficient hybrid music recommender system using an incrementally trainable probabilistic generative model[J]. Audio, Speech, and Language Processing, IEEE Transactions on, 2008, 16(2):435-447.
[6] GOLBECK J, MASSA P, AVESANI P. Trust metrics in recommender systems[M] //GOLBECK J. Computing with Social Trust. London: Springer, 2009: 259-285.
[7] JENSEN C, POSLAD S, DIMITRAKOS T, et al. Using trust in recommender systems: an experimental analysis[M] //JENSEN C, POSLAD S, DIMITRAKOS T. Trust Management. Berlin: Springer, 2004:221-235.
[8] 张富国. 用户多兴趣下基于信任的协同过滤算法研究[J]. 小型微型计算机系统, 2008,29(8):1415-1419. ZHANG Fuguo. Research on trust based collaborative filtering algorithm for users multiple interests[J]. Journal of Chinese Computer Systems, 2008, 29(8):1415-1419
[9] 俞琰, 邱广华. 融合社会网络的协同过滤推荐算法研究[J]. 现代图书情报技术, 2012(6):54-59. YU Yan, QIU Guanghua. Research on collaborative filtering recommendation algorithm by fusing social network[J].New Technology of Library and Information Service, 2012(6):54-59.
[10] RENDLE S. Factorization machines[C] //Proceedings of the 10th IEEE International Conference on Data Mining.New York:IEEE Press, 2010:995-1000.
[11] 刘建国, 周涛, 郭强,等. 个性化推荐系统评价方法综述[J]. 复杂系统与复杂性科学, 2009,6(3):1-10. LIU Jianguo, ZHOU Tao, GUO Qiang, et al. Overview of the evaluated algorithms for the personal recommendation systems[J].Complex Systems and Complexity Science, 2009, 6(3):1-10.
[1] ZHANG Jian-biao, LI Zhi-gang, LIU Guo-jie, WANG Chao, WANG Wei. Process active dynamic measurement method for Windows environment [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(7): 46-50.
[2] CUI Zhao-yang, SUN Jia-qi, XU Song-yan, JIANG Xin. A secure clustering algorithm of Ad Hoc network for colony UAVs [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(7): 51-59.
[3] SUN Liang, CHEN Xiao-chun, ZHONG Yang, LIN Zhi-peng, REN Tong. Secure startup mechanism of server based on trusted BMC [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(1): 89-94.
[4] CUI Xiao-yu, ZHAO Bo, FAN Pei-ru, XIAO Yu. A key management mechanism basedon TrustZone architecture [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(6): 92-98.
[5] LI Xiao-ce, PAN Xiao-zhong, MAI Tao-tao. Multi-component property based remote attestation [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(9): 53-58.
[6] PENG Qiu-fang, LIU Yang. Research of gender prediciton based on SVM with E-commerce data [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(7): 74-80.
[7] ZHAO Bin, HE Jing-sha, ZHANG Yi-xuan. The method of determining decision attribute weight based on information entropy and membership [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(3): 86-90.
[8] LIU Da-fu, SU Yang. Research on evidence-based software trustworthiness metrics model [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(11): 58-65.
[9] ZHU Rui. E-commerce community clustering model based on trust [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2015, 50(05): 18-22.
[10] JIANG Wei-jin, XU Yu-hui, GUO Hong, XU Yu-sheng. A multi-dimensional evidence dynamic trust computing model based on multi-agent [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2015, 50(01): 1-11.
[11] LÜ Meng, LIU Zhe, LIU Jian-wei. A trusted inter-domain access control scheme for enterprise WLAN [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(11): 82-88.
[12] YANG Xiao-hui, WANG Hong, JIANG Li-jun, CHANG Si-yuan. A cross-domain access control model of Web service based on trust measurement [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(09): 115-122.
[13] LI Guo-qing. A trust evaluation model based on connection number for wireless sensor networks [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(09): 123-128.
[14] ZHAO Bin, HE Jing-sha, ZHANG Yi-xuan, ZHAI Peng, LIU Gong-zheng, ZHOU Shi-yi. Analysis of multi-factor in trust evaluation of open network [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(09): 103-108.
[15] LUO hai-yan, LÜ Ping, LIU Lin-zhong, YANG Xun. Enterprises trust comprehensive evaluation based on fussy rough AHP in cloud computing [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(08): 111-117.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright 2018 © Editorial office of JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE)
Supported by Beijing Magtech Co.Ltd