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.
Similar content being viewed by others
References
Xiao Q, Xie H (2015) A social tag recommendation method alleviating cold start based on probabilistic graphical model. IJES 7(2):162–169
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):729–748
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 29
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):53–63
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):178–185
Wang JG, Huang JZ, Wu D, Guo J, Lan Y (2016) An incremental model on search engine query recommendation. Neurocomputing 218:423–431
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 2766–2771
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):355–369
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 463–472
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 457–462
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):1351–1371
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 2101–2106
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 Society
Huang G, Song S, Gupta JND, Wu C (2014) Semi-supervised and unsupervised extreme learning machines. IEEE T Cybern 44(12):2405–2417
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, IEEE
Sun Y, Yuan Y, Wang G (2014) Extreme learning machine for classification over uncertain data. Neurocomputing 128:500–506
Zong W, Huang G-B (2014) Learning to rank with extreme learning machine. Neural Process Lett 39(2):155–166
Sun Y, Yuan Y, Wang G (2011) An os-elm based distributed ensemble classification framework in p2p networks. Neurocomputing 74(16):2438–2443
Wang XZ, Wang R, Xu C (2017) Discovering the relationship between generalization and uncertainty by incorporating complexity of classification. IEEE Trans Cybern PP(99):1–13
Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501
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):1–10
Zhu H, Wang X (2017) A cost-sensitive semi-supervised learning model based on uncertainty. Neurocomputing 251:106–114
Liu J, Chen Y, Liu M, Zhao Z (2011) SELM: semi-supervised ELM with application in sparse calibrated location estimation. Neurocomputing 74(16):2566–2572
Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396
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 849–856
Page L (1998) The pagerank citation ranking : bringing order to the web, online manuscript. http://www-db.stanford.edu/-backrub/pageranksub.ps
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 1697–1700
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).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ding, L., Han, B., Wang, S. et al. User-centered recommendation using US-ELM based on dynamic graph model in E-commerce. Int. J. Mach. Learn. & Cyber. 10, 693–703 (2019). https://doi.org/10.1007/s13042-017-0751-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13042-017-0751-z