Advertisement

Social recommendation algorithm based on stochastic gradient matrix decomposition in social network

  • Tian-wu Zhang
  • Wei-ping Li
  • Lu Wang
  • Jie Yang
Original Research
  • 9 Downloads

Abstract

The revenue of an e-commerce system is affected directly by the prediction accuracy of recommendation system. Although recommendation systems have been comprehensively analyzed in the past decade, the study of social-based recommendation systems just started. In this paper, aiming at providing a general method for improving recommendation systems by incorporating social network information, we propose a social recommendation algorithm based on stochastic gradient matrix decomposition in social network so as to improve the prediction accuracy. This paper considered the social network as auxiliary information, and proposed a matrix factorization based on social recommendation algorithm, which systematically illustrate how to design a matrix factorization objective function with social regularization. It constructed a matrix with the social network and the user scoring matrix, and proposed a stochastic gradient descent algorithm for matrix factorization. The empirical analysis on two large datasets demonstrates our proposed algorithm has lower prediction error, and is obviously better than other state-of-the-art methods.

Keywords

Matrix decomposition Recommendation system Social network Stochastic gradient 

Notes

Acknowledgements

The Scientific and Technological Research Program of Henan Province, China under Grant no. 172102210111. The Ministry of Public Security Technical Research Plan, under Grant no. 2016JSYJB38. The Scientific and Technological Research Program of Henan Province, China under Grant no. 172102210441.

References

  1. Agarwal D, Chen B-C (2010) fLDA: matrix factorization through latent dirichlet allocation. In: Proc. of WSDM2010, New York, New York, USA, pp 91–100Google Scholar
  2. Bedi P, Kaur H, Marwaha S (2007) Trust based recommender system for semantic web. In Proc. of IJCAI, pp 2677–2682, 2007Google Scholar
  3. Bellogín A, Castells P, Cantador I (2013) Improving memory-based collaborative filtering by neighbour selection based on user preference overlap. In: Proceedings of the 10th conference on open research areas in information retrieval, pp 145–148Google Scholar
  4. Golbeck J (2005) Computing and applying trust in web-based social networks. PhD thesis, University of Maryland College ParkGoogle Scholar
  5. Hailing X, Xiao W, Xiaodong L et al (2009) Research on internet recommendation system. J Softw 20(2):350–362CrossRefGoogle Scholar
  6. Hsu FM, Lin YT, Ho TK (2012) Design and implementation of an intelligent recommendation system for tourist attractions: the integration of EBM model, Bayesian network and Google Maps. Expert Syst Appl 39(3):3257–3264CrossRefGoogle Scholar
  7. Huang Chuanguang Y, Jian W, Jing et al (2010) Research on collaborative filtering recommendation algorithm for indefinite neighbors. Comput Sci 33(8):1369–1377Google Scholar
  8. Jamali M, Ester M (2009) TrustWalker: a random walk model for combining trust-based and item-based recommendation. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 397–406Google Scholar
  9. Ma H, Yang H, Lyu MR et al (2008) Sorec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM conference on information and knowledge management, ACM, pp 931–940Google Scholar
  10. Ma H, King I, Lyu MR (2009) Learning to recommend with social trust ensemble. In: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval. ACM, pp 203–210Google Scholar
  11. Massa P, Avesani P (2007) Trust-aware recommender systems. In: Proceedings of the 2007 ACM conference on recommender systems, ACM, pp 17–24Google Scholar
  12. Park DH, Kim HK, Choi IY et al (2012) A literature review and classification of recommender systems research. Expert Syst Appl 39(11):10059–10072CrossRefGoogle Scholar
  13. Sarwar B, Karypis G, Konstan J et al (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on world wide web, ACM, pp 285–295Google Scholar
  14. Sattarpour T, Nazarpour D, Golshannavaz S et al (2018) A multi-objective hybrid GA and TOPSIS approach for sizing and siting of DG and RTU in smart distribution grids. J Ambient Intell Human Comput 9:105.  https://doi.org/10.1007/s12652-016-0418-8 CrossRefGoogle Scholar
  15. Tsai CF, Hung C (2012) Cluster ensembles in collaborative filtering recommendation. Appl Soft Comput 12(4):1417–1425CrossRefGoogle Scholar
  16. Tu D, Shu C, Yu H (2013) Context advertisement algorithm based on joint probability matrix decomposition. J Softw 24(3):454–464CrossRefGoogle Scholar
  17. Wang L, Meng X, Zhang Y (2012) Context perception recommendation system. J Softw 23(1):103–115CrossRefGoogle Scholar
  18. Xia KJ, Wang JQ (2017) A novel medical image enhancement algorithm based on improvement correction strategy in wavelet transform domain. Clust Comput.  https://doi.org/10.1007/s10586-017-1264-y CrossRefGoogle Scholar
  19. Xia KJ, Wang JQ, Wu Y (2017) Robust Alzheimer disease classification based on feature integration fusion model for magnetic. J Med Imaging Health Inf 7:1–6CrossRefGoogle Scholar
  20. Xia KJ, Yin HS, Wang JQ (2018) A novel improved deep convolutional neural network model for medical image fusion. Clust Comput 3:1–13Google Scholar
  21. Zhang Y, Zhang M, Liu Y et al (2013) Localized matrix factorization for recommendation based on matrix block diagonal forms. In: Proceedings of the 22nd international conference on world wide web, pp 1511–1520Google Scholar
  22. Zhou T, Shan H, Banerjee A et al (2012) Kernelized probabilistic matrix factorization: exploiting graphs and side information. In: Proceedings of the 2012 SIAM international conference on data mining (SDM), vol 36. pp 403–414Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Tian-wu Zhang
    • 1
  • Wei-ping Li
    • 2
    • 4
  • Lu Wang
    • 3
    • 4
  • Jie Yang
    • 2
  1. 1.School of ComputingHenan University of EngineeringZhengzhouChina
  2. 2.School of Information EngineeringWuhan University of TechnologyWuhanChina
  3. 3.Department of Electrical AutomationShanghai Maritime UniversityShanghaiChina
  4. 4.Department of Police TechnologyRailway Police CollegeZhengzhouChina

Personalised recommendations