GeoDAE for Pointofinterest Recommendation

doi:10.3969/j.issn.10062475.2018.04.014

Abstract

Abstract: Personalized pointofinterest (POI) recommendation is crucial to the development of locationbased social networks (LBSNs). It not only helps users explore new places but also enables thirdparty services to better provide service. Previous studies on this topic treat all POIs as equal. Learning preferences within category makes sense, but the scale in which the frequency of checkins operates is not comparable across categories. In this paper, we transform the checkin frequency into categorybased preference according to TFIDF theory. And then we propose a GeoDAE model for the geographical proximity among POIs. The experimental results based on datasets from realworld LBSNs show that the proposed model achieves better performance than other stateoftheart methods, and the proposed model is a better alternative for POI recommendation.

Key words: POI recommendation, auto encoder, geographical proximity

CLC Number:

TP18

ZHANG Wenxiang. GeoDAE for Pointofinterest Recommendation[J]. Computer and Modernization, doi: 10.3969/j.issn.10062475.2018.04.014.

References

［1］〖JP3〗Li Nan, Chen Guanling. Sharing location in online social networks［J］. IEEE Network, 2010,24(5):2025. 
［2］ Liang Tingpeng, Lai HungJen, Ku Yicheng. Personalized content recommendation and user satisfaction: Theoretical synthesis and empirical findings［J］. Journal of Management Information Systems, 2006,23(3):4570.
［3］ Cheng Chen, Yang Haiqin, King I, et al. A unified pointofinterest recommendation framework in locationbased social networks［J］. ACM Transactions on Intelligent Systems and Technology, 2016,8(1): Article No. 10.
［4］胥皇,於志文,封云,等. 基于LBSN的个性化旅游包推荐系统［J］. 计算机与现代化, 2014(1):186191.
［5］ Liu Bin, Xiong Hui, Papadimitriou S, et al. A general geographical probabilistic factor model for point of interest recommendation［J］. IEEE Transactions on Knowledge and Data Engineering, 2015,27(5):11671179.
［6］ LeCun Y, Bengio Y, Hinton G. Deep learning［J］. Nature, 2015,521(7553): 436444.
［7］ Ye Mao, Yin Peifeng, Lee W C, et al. Exploiting geographical influence for collaborative pointofinterest recommendation［C］// Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2011:325334.
［8］ Brockmann D, Hufnagel L, Geisel T. The scaling laws of human travel［J］. Nature, 2006,439(7075):462465.
［9］ Gonzalez M C, Hidalgo C A, Barabasi A L. Understanding individual human mobility patterns［J］. Nature, 2008,453(7196):779782.
［10］Zhang Jiadong, Chow C Y. CoRe: Exploiting the personalized influence of twodimensional geographic coordinates for location recommendations［J］. Information Sciences, 2015,293:163181.
［11］Salakhutdinov R, Mnih A, Hinton G. Restricted Boltzmann machines for collaborative filtering［C］// Proceedings of the 24th International Conference on Machine Learning. 2007:791798.
［12］Zhou Ningnan, Zhao W X, Zhang Xiao, et al. A general multicontext embedding model for mining human trajectory data［J］. IEEE Transactions on Knowledge and Data Engineering, 2016,28(8):19451958.
［13］Song Xuan, Zhang Quanshi, Sekimoto Y, et al. Prediction and simulation of human mobility following natural disasters［J］. ACM Transactions on Intelligent Systems and Technology, 2017,8(2): Article No. 29.
［14］Miller H J. Toblers first law and spatial analysis［J］. Annals of the Association of American Geographers, 2004,94(2):284289.
［15］Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks［J］. Science, 2006,313(5786):504507.
［16］牛玉虎. 卷积稀疏自编码神经网络［J］. 计算机与现代化, 2017(2):2229.
［17］曲建岭,杜辰飞,邸亚洲,等. 深度自动编码器的研究与展望［J］. 计算机与现代化, 2014(8):128134.

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