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A New Point-of-Interest Classification Model with an Extreme Learning Machine

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Abstract

With the increasing popularity of location-based social networks (LBSNs), an increasing number of people are sharing their locations with friends through check-in activities. Point-of-interest (POI) recommendation, in which new places are suggested to users, is one of the most important tasks in LBSNs. However, after recommendation, it is also of interest to consider whether a user will frequently visit a recommended POI, which may have significant implications regarding the user’s daily mobility behavior or personal preferences. Therefore, in this paper, we propose a new POI classification problem in which the POIs recommended to a user are divided into four classes according to the user’s predicted future check-in frequency: daily check-in POIs, weekly check-in POIs, monthly check-in POIs, and yearly check-in POIs. To solve this POI classification problem, we also propose a new POI classification model called POIC-ELM. In the POIC-ELM model, we first extract nine features related to three factors: each POI itself, the user’s personality, and the user’s social relationships. Then, we use these features to train a POI classifier based on an extreme learning machine (ELM), which is one of the most popular types of classifiers among state-of-the-art classification techniques. A series of experiments show that the effectiveness and efficiency of POIC-ELM are superior to those of other methods. The POIC-ELM model is a valid method for solving the POI classification problem.

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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):525–65.

    Article  Google Scholar 

  2. Sohail A, Taniar D, Züfle A, Park J-H. Query processing in location-based social networks. Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia, April 3-7, 2017, 1379–1381; 2017.

  3. Ference G, Ye M, Lee W-C. Location recommendation for out-of-town users in location-based social networks. 22nd ACM international conference on information and knowledge management, CIKM’13, San Francisco, CA, USA, October 27 - November 1, 2013, 721–726; 2013.

  4. Ye M, Yin P, Lee W-C. Location recommendation for location-based social networks. 18Th ACM SIGSPATIAL international symposium on advances in geographic information systems, ACM-GIS 2010, november 3-5, 2010, San Jose, CA, USA, Proceedings, 458–461; 2010.

  5. Ye M, Yin P, Lee W-C, Lee DL. Exploiting geographical influence for collaborative point-of-interest recommendation. Proceeding of the 34th international ACM SIGIR conference on research and development in information retrieval, SIGIR 2011, Beijing, China, july 25-29, 2011, 325–334; 2011.

  6. Cheng C, Yang H, King I, Lyu MR. Fused matrix factorization with geographical and social influence in location-based social networks. Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, July 22-26, 2012, Toronto, Ontario, Canada; 2012.

  7. Zhao X, Ma Z, Zhang Z. 2017. A novel recommendation system in location-based social networks using distributed ELM. Memetic Comput, 1–11.

  8. Gao H, Tang J, Hu X, Liu H. Exploring temporal effects for location recommendation on location-based social networks. Seventh ACM conference on recommender systems, RecSys ’13, hong kong, china, october 12-16, 2013, 93–100; 2013.

  9. Wong P-K, Gao XH, Wong KI, Vong C-M. An analytical study on reasoning of extreme learning machine for classification from its inductive bias. Cogn Comput 2016;8(4):746–56.

    Article  Google Scholar 

  10. Scardapane S, Uncini A. Semi-supervised echo state networks for audio classification. Cogn Comput 2017;9 (1):125–35.

    Article  Google Scholar 

  11. Torabi A, Jahromy FZ, Daliri MR. Semantic category-based classification using nonlinear features and wavelet coefficients of brain signals. Cogn Comput 2017;9(5):702–11.

    Article  Google Scholar 

  12. Zhang Z, Zhao X, Wang G. FE-ELM A new friend recommendation model with extreme learning machine. Cogn Comput 2017;9(5):659–70.

    Article  Google Scholar 

  13. Guo T, Zhang L, Tan X. Neuron pruning-based discriminative extreme learning machine for pattern classification. Cogn Comput 2017;9(4):581–95.

    Article  Google Scholar 

  14. 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):150–60.

    Article  Google Scholar 

  15. Huang G-B, Siew C-K. Extreme learning machine: RBF network case. Control, automation, robotics and vision conference, 2004. ICARCV 2004 8th, 2:1029–1036. IEEE; 2004.

  16. 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 

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

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Huang G-B. What are extreme learning machines? Filling the gap between Frank Rosenblatt’s dream and John von Neumann’s puzzle. Cogn Comput 2015;7(3):263–78.

    Article  Google Scholar 

  21. Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995;20(3):273–97.

    Google Scholar 

  22. Zhang X, Ding S, Xue Y. An improved multiple birth support vector machine for pattern classification. Neurocomputing 2017;225:119–28.

    Article  Google Scholar 

  23. Xia J, Chanussot J, Du P, He X. Rotation-based support vector machine ensemble in classification of hyperspectral data with limited training samples. IEEE Geosci Remote Sens Lett 2016;54(3):1519–31.

    Article  Google Scholar 

  24. Emrouznejad A. Back-propagation DEA. Proceedings of the 2006 International Conference on Data Mining, DMIN 2006, Las Vegas, Nevada, USA, June 26-29, 2006, 317–320; 2006.

  25. Jiang J, Zhang J, Yang G, Zhang D, Zhang L. Application of back propagation neural network in the classification of high resolution remote sensing image: take remote sensing image of Beijing for instance. The 18th international conference on geoinformatics: GIScience in change, geoinformatics 2010, Peking University, Beijing, China, June, 18-20, 2010, 1–6 ; 2010.

  26. Chen Y, Chan AB. Enhanced figure-ground classification with background prior propagation. IEEE Trans Image Process 2015;24(3):873–85.

    Article  PubMed  Google Scholar 

  27. Wang S, Deng C, Lin W, Huang G-B, Zhao B. NMF-based image quality assessment using extreme learning machine. IEEE Trans Cybernetics 2017;47(1):232–43.

    Article  Google Scholar 

  28. Deng C, Wang S, Li Z, Huang GB, Lin W. Content-insensitive blind image blurriness assessment using weibull statistics and sparse extreme learning machine. IEEE Trans Syst Man, Cybernetics: Syst 2017;PP(99): 1–12.

    Google Scholar 

  29. Cheng Y, Yuan Y, Chen L, Wang G, Giraud-Carrier CG, Sun Y. Distr: A distributed method for the reachability query over large uncertain graphs. IEEE Trans Parallel Distrib Syst 2016;27(11):3172–85.

    Article  Google Scholar 

  30. Zhai M-Y, Yu R-H, Zhang S-F, Zhai J-H. Feature selection based on extreme learning machine. International conference on machine learning and cybernetics, Proceedings ICMLC 2012, Xian, Shaanxi, China, July 15-17, 2012, 157–162; 2012.

  31. Zhu R, Wang B, Yang X, Zheng B, Wang G. SAP: Improving continuous top-k queries over streaming data. IEEE Trans Knowl Data Eng 2017;29(6):1310–28.

    Article  Google Scholar 

  32. Cheng Y, Ye Y, Chen L, Giraud-Carrier C, Wang Guoren. Complex event-participant planning and its incremental variant. IEEE International conference on data engineering, 859–870; 2017.

  33. Tong Y, Chen L, Zhou Z, Jagadish HV, Shou L, Weifeng Lv. Slade: A smart large-scale task decomposer in crowdsourcing. IEEE Trans Knowl Data Eng 2018;PP(99):1–1.

    Google Scholar 

  34. Liu X, Wang L, Huang G-B, Zhang J, Yin J. Multiple kernel extreme learning machine. Neurocomputing 2015;149:253–64.

    Article  Google Scholar 

  35. Liu X, Wang L, Yin J, En Z, Zhang J. An efficient approach to integrating radius information into multiple kernel learning. IEEE Trans Cybernetics 2013;43(2):557–69.

    Article  Google Scholar 

  36. Liu X, Li M, Wang L, Dou Y, Yin J, En Z. Multiple kernel k-means with incomplete kernels. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA, 2259–2265 ; 2017.

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Funding

This research was partially supported by the National Natural Science Foundation of China under Grant Nos. 61672145, 61572121, 61602323, 61702086, and U1401256.

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Authors and Affiliations

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

    Zhen Zhang, Xiangguo Zhao & Guoren Wang

  2. Sino-Dutch Biomedical and Information Engineering, Northeastern University, Shenyang, Liaoning, China

    Xin Bi

Authors
  1. Zhen Zhang
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  2. Xiangguo Zhao
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  3. Guoren Wang
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  4. Xin Bi
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Correspondence to Xiangguo Zhao.

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This article does not report any studies involving human participants and/or animals by any of the authors. Informed consent was obtained from all individual participants.

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Zhang, Z., Zhao, X., Wang, G. et al. A New Point-of-Interest Classification Model with an Extreme Learning Machine. Cogn Comput 10, 951–964 (2018). https://doi.org/10.1007/s12559-018-9599-0

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