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FE-ELM: A New Friend Recommendation Model with Extreme Learning Machine

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Abstract

Friend recommendation is one of the most popular services in location-based social network (LBSN) platforms, which recommends interested or familiar people to users. Except for the original social property and textual property in social networks, LBSN specially owns the spatial-temporal property. However, none of the existing methods fully utilized all the three properties (i.e., just one or two), which may lead to the low recommendation accuracy. Moreover, these existing methods are usually inefficient. In this paper, we propose a new friend recommendation model to solve the above shortcomings of the existing methods, called feature extraction-extreme learning machine (FE-ELM), where friend recommendation is regarded as a binary classification problem. Classification is an important task in cognitive computation community. First, we use new strategies in our FE-ELM model to extract the spatial-temporal feature, social feature, and textual feature. These features make full use of all above properties of LBSN and ensure the recommendation accuracy. Second, our FE-ELM model also takes advantage of the extreme learning machine (ELM) classifier. ELM has fast learning speed and ensures the recommendation efficiency. Extensive experiments verify the accuracy and efficiency of FE-ELM model.

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References

  1. Bao J, Zheng Y, Wilkie D, Mokbel M. Recommendations in location-based social networks: a survey. GeoInformatica 2015;19(3):525–565.

    Article  Google Scholar 

  2. Hruschka D J, Henrich J. Friendship, cliquishness, and the emergence of cooperation. J Theor Biol 2006; 239(1):1–15.

    Article  PubMed  Google Scholar 

  3. Zheng Y, Zhang L, Ma Z, Xie X, Ma W-Y. Recommending friends and locations based on individual location history. ACM Trans Web (TWEB) 2011;5(1):5.

    Google Scholar 

  4. Sui X, Chen Z, Ma J. Location sensitive friend recommendation in social network. Springer International Publishing Cham; 2015. p. 316–327 .

  5. Bagci H, Karagoz P. Context-aware friend recommendation for location based social networks using random walk. In: Proceedings of the 25th international conference on world wide web, WWW 2016. Montreal, Canada, April 11-15, 2016, Companion Volume; 2016. p. 531–536.

  6. Han D, Yachao H, Ai S, Wang G. Uncertain graph classification based on extreme learning machine. Cogn Comput 2015;7(3):346–358.

    Article  Google Scholar 

  7. Cao K, Wang G, Han D, Ning J, Zhang X. Classification of uncertain data streams based on extreme learning machine. Cogn Comput 2015;7(1):150–160.

    Article  Google Scholar 

  8. Wang B, Zhu R, Luo S, Yang X, Wang G. H-MRST: a novel framework for supporting probability degree range query using extreme learning machine. Cogn Comput 2017;9(1):68–80.

    Article  Google Scholar 

  9. Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995;20(3):273–297.

    Google Scholar 

  10. Emrouznejad A. Back-propagation DEA. In: Proceedings of the 2006 international conference on data mining, DMIN 2006. Las Vegas; 2006. p. 317–320.

  11. Huang G-B, Siew C-K. Extreme learning machine: Rbf network case. Control, automation, robotics and vision conference, 2004. ICARCV 2004 8th. IEEE; 2004. p. 1029–1036.

  12. Huang G-B, Siew C-K. Extreme learning machine with randomly assigned rbf kernels. Int J Inf Technol 2005; 11(1):16–24.

    CAS  Google Scholar 

  13. Huang G-B, Zhu Q-Y, Siew C-K. Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE International joint conference on neural networks, 2004. Proceedings; 2004. vol. 2, p. 985–990.

  14. Huang G-B, Zhu Q-Y, Siew C-K. Extreme learning machine: theory and applications. Neurocomputing 2006;70(1):489–501.

    Article  Google Scholar 

  15. Xiaoxuan L, Zou H, Zhou H, Xie L, Huang G-B. Robust extreme learning machine with its application to indoor positioning. IEEE Trans Cybern 2016;46(1):194–205.

    Article  Google Scholar 

  16. Lu E H-C, Tseng VS, Yu P S. Mining cluster-based temporal mobile sequential patterns in location-based service environments. IEEE Trans Knowl Data Eng 2011;23(6):914–927.

    Article  Google Scholar 

  17. Chen J, Geyer W, Dugan C, Muller Mi, Guy I. Make new friends, but keep the old: recommending people on social networking sites. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM; 2008. p. 201–210.

  18. Linyuan L, Zhou T. Link prediction in complex networks: a survey. Phys A: Stat Mech Appl 2011;390(6): 1150–1170.

    Article  Google Scholar 

  19. Deng S, Huang L, Guandong X. Social network-based service recommendation with trust enhancement. Expert Syst Appl 2014;41(18):8075–8084.

    Article  Google Scholar 

  20. Nguyen T T, Hui P-M, Maxwell Harper F, Terveen L, Konstan J A. Exploring the filter bubble: the effect of using recommender systems on content diversity. In: Proceedings of the 23rd international conference on world wide web. ACM; 2014. p. 677–686.

  21. Cranshaw J, Toch E, Hong J, Kittur A, Sadeh N. Bridging the gap between physical location and online social networks. In: Proceedings of the 12th ACM international conference on ubiquitous computing. ACM; 2010. p. 119–128.

  22. Li N, Chen G. Multi-layered friendship modeling for location-based mobile social networks. In: Mobile and ubiquitous systems: networking & services, MobiQuitous, 2009. MobiQuitous’ 09. 6th annual international. IEEE; 2009. p. 1–10.

  23. Cheng Y, Ye Y, Chen L, Wang G, Giraud-carrier C G, Sun Y. Distr: a distributed method for the reachability query over large uncertain graphs. IEEE Trans Parallel Distrib Syst 2016;27(11):3172–3185.

    Article  Google Scholar 

  24. Wang H, Schmid C. Action recognition with improved trajectories. In: IEEE International conference on computer vision, ICCV 2013. Sydney; 2013. p. 3551–3558.

  25. Gao Z, Zhang H, Xu G P, Xue Y B, Hauptmann A G. Multi-view discriminative and structured dictionary learning with group sparsity for human action recognition. Signal Process 2015;112:83–97.

    Article  Google Scholar 

  26. Liu A, Su Y, Nie W, Kankanhalli M S. Hierarchical clustering multi-task learning for joint human action grouping and recognition. IEEE Trans Pattern Anal Mach Intell 2017;39(1):102–114.

    Article  PubMed  Google Scholar 

  27. Rahmani H, Mian A S . 3d action recognition from novel viewpoints. In: 2016 IEEE conference on computer vision and pattern recognition, CVPR 2016. Las Vegas; 2016. p. 1506–1515.

  28. Haveliwala T H. Topic-sensitive pagerank. In: Proceedings of the 11th international conference on world wide web. ACM; 2002. p. 517–526.

  29. Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 2005;17(6):734–749.

    Article  Google Scholar 

  30. Nie F, Huang H, Cai X, Ding CH, Shawe-Taylor J, Zemel RS. Efficient and robust feature selection via joint l2,1-norms minimization. In: Lafferty JD, Williams CKI, and Culotta A, editors. Advances in neural information processing systems. Curran Associates, Inc.; 2010. p. 1813–1821.

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Acknowledgments

This research is partially supported by the National Natural Science Foundation of China under Grant Nos. 61672145, 61572121, 61602323, and U1401256, and the China Postdoctoral Science Foundation under Grant No. 2016M591455.

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  1. College of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China

    Zhen Zhang, Xiangguo Zhao & Guoren Wang

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  1. Zhen Zhang
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  2. Xiangguo Zhao
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  3. Guoren Wang
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Correspondence to Zhen Zhang.

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Zhang, Z., Zhao, X. & Wang, G. FE-ELM: A New Friend Recommendation Model with Extreme Learning Machine. Cogn Comput 9, 659–670 (2017). https://doi.org/10.1007/s12559-017-9484-2

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