User-centered recommendation using US-ELM based on dynamic graph model in E-commerce

  • Linlin Ding
  • Baishuo Han
  • Shu Wang
  • Xiaoguang Li
  • Baoyan Song
Original Article
First Online:
Received:
Accepted:

Abstract

The recommender systems can gain the needs and interests of users by analyzing the user history data and then help the users making decisions on appropriate choices in E-commerce. However, with the increasing of data volume and the popularization of information network, the participation of users in E-commerce activities is growing deeply. How to analyze the user preferences and make a user-centered efficient recommendation is an urgent problem to be further researched. In this paper, we first propose the user-centered recommendation based on dynamic graph model to express the user preferences and gain the user preference vectors for recommendation. Then, after gaining the user preferences vectors, we propose the user clustering algorithm using US-ELM to cluster the users into different clusters. Last, we provide two recommendation algorithms, which can present top-k recommendation, respectively the group recommendation and personal recommendation. With the extensive experiments, our recommendation algorithms can effectively express the user preferences and reach a good performance.

Keywords

US-ELM Dynamic graph Recommendation algorithm User preference 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

This work is supported by National Natural Science Foundation of China (nos. 61472169, 61472069, 61502215, 61402089), Science Research Normal Fund of Liaoning Province Education Department (no. L2015193), Doctoral Scientific Research Start Foundation of Liaoning Province (no. 201501127), National Key Research and Development Program of China (no. 2016YFC0801406).

References

  1. 1.
    Xiao Q, Xie H (2015) A social tag recommendation method alleviating cold start based on probabilistic graphical model. IJES 7(2):162–169CrossRefGoogle Scholar
  2. 2.
    Cao J, Wu Z, Mao B, Zhang Y (2013) Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system. World Wide Web 16(5–6):729–748CrossRefGoogle Scholar
  3. 3.
    Cui L, Dong L, Fu X, Wen Z, Lu N, Zhang G (2017) A video recommendation algorithm based on the combination of video content and social network. Concurr Comput Pract Exp 29Google Scholar
  4. 4.
    Cui L, Ou P, Fu X, Wen Z, Lu N (2017) A novel multi-objective evolutionary algorithm for recommendation systems. J Parallel Distrib Comput 103(C):53–63Google Scholar
  5. 5.
    Liu M, Pan W, Liu M, Chen Y, Peng X, Ming Z (2017) Mixed similarity learning for recommendation with implicit feedback. Knowl Based Syst 119(C):178–185Google Scholar
  6. 6.
    Wang JG, Huang JZ, Wu D, Guo J, Lan Y (2016) An incremental model on search engine query recommendation. Neurocomputing 218:423–431CrossRefGoogle Scholar
  7. 7.
    Gori M, Pucci A (2007) Itemrank: a random-walk based scoring algorithm for recommender engines. In: IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6-12, 2007, pp 2766–2771Google Scholar
  8. 8.
    Fouss F, Pirotte A, Renders J, Saerens M (2007) Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans Knowl Data Eng 19(3):355–369CrossRefGoogle Scholar
  9. 9.
    Yang D, He J, Qin H, Xiao Y, Wang W (2015) A graph-based recommendation across heterogeneous domains. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM 2015, Melbourne, VIC, Australia, October 19–23, 2015, pp 463–472Google Scholar
  10. 10.
    Cheng H, Tan P, Sticklen J, Punch WF (2007) Recommendation via query centered random walk on k-partite graph. In: Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), October 28—31, 2007, Omaha, Nebraska, USA, pp 457–462Google Scholar
  11. 11.
    Yao W, He J, Huang G, Cao J, Zhang Y (2015) A graph-based model for context-aware recommendation using implicit feedback data. World Wide Web 18(5):1351–1371CrossRefGoogle Scholar
  12. 12.
    Kang Z, Peng C, Yang M, Cheng Q (2016) Top-n recommendation on graphs. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, IN, USA, October 24–28, 2016, pp 2101–2106Google Scholar
  13. 13.
    Gehrke J, Lehner W, Shim K, Cha SK, Lohman GM (eds) (2015) 31st IEEE International Conference on Data Engineering, ICDE 2015, Seoul, South Korea, April 13–17, 2015, IEEE Computer SocietyGoogle Scholar
  14. 14.
    Huang G, Song S, Gupta JND, Wu C (2014) Semi-supervised and unsupervised extreme learning machines. IEEE T Cybern 44(12):2405–2417CrossRefGoogle Scholar
  15. 15.
    Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE International Joint Conference on Neural Networks, 2004. Proceedings, vol 2, pp 985–990, IEEEGoogle Scholar
  16. 16.
    Sun Y, Yuan Y, Wang G (2014) Extreme learning machine for classification over uncertain data. Neurocomputing 128:500–506CrossRefGoogle Scholar
  17. 17.
    Zong W, Huang G-B (2014) Learning to rank with extreme learning machine. Neural Process Lett 39(2):155–166CrossRefGoogle Scholar
  18. 18.
    Sun Y, Yuan Y, Wang G (2011) An os-elm based distributed ensemble classification framework in p2p networks. Neurocomputing 74(16):2438–2443CrossRefGoogle Scholar
  19. 19.
    Wang XZ, Wang R, Xu C (2017) Discovering the relationship between generalization and uncertainty by incorporating complexity of classification. IEEE Trans Cybern PP(99):1–13Google Scholar
  20. 20.
    Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501CrossRefGoogle Scholar
  21. 21.
    Wang XZ, Zhang T, Wang R (2017) Noniterative deep learning: Incorporating restricted boltzmann machine into multilayer random weight neural networks. IEEE Trans Syst Man Cybern Syst PP(99):1–10Google Scholar
  22. 22.
    Zhu H, Wang X (2017) A cost-sensitive semi-supervised learning model based on uncertainty. Neurocomputing 251:106–114CrossRefGoogle Scholar
  23. 23.
    Liu J, Chen Y, Liu M, Zhao Z (2011) SELM: semi-supervised ELM with application in sparse calibrated location estimation. Neurocomputing 74(16):2566–2572CrossRefGoogle Scholar
  24. 24.
    Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396CrossRefMATHGoogle Scholar
  25. 25.
    Ng AY, Jordan MI, Weiss Y (2001) On spectral clustering: Analysis and an algorithm. In: Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, December 3–8, 2001, Vancouver, British Columbia, Canada], pp 849–856Google Scholar
  26. 26.
    Page L (1998) The pagerank citation ranking : bringing order to the web, online manuscript. http://www-db.stanford.edu/-backrub/pageranksub.psGoogle Scholar
  27. 27.
    Liu Q, Chen E, Xiong H, Ding CHQ (2010) Exploiting user interests for collaborative filtering: interests expansion via personalized ranking. In: Proceedings of the 19th ACM Conference on Information and Knowledge Management, CIKM 2010, Toronto, Ontario, Canada, October 26–30, 2010, pp 1697–1700Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Linlin Ding
    • 1
  • Baishuo Han
    • 1
  • Shu Wang
    • 1
  • Xiaoguang Li
    • 1
  • Baoyan Song
    • 1
  1. 1.School of InformationLiaoning UniversityShenyangChina

Personalised recommendations