[1] MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases and their compositionality[C]//Proceedings of the 2013 Conference on Neural Information Processing Systems. Cambridge:MIT Press,2013:3111-3119. [2] 常亮, 孙文平, 张伟涛, 等. 旅游路线规划研究综述[J]. 智能系统学报,2019,14(1):82-92.(CHANG L,SUN W P,ZHANG W T,et al. Review of tourism route planning[J]. CAAI Transactions on Intelligent Systems,2019,14(1):82-92.) [3] ZHENG Y,ZHANG L,MA Z,et al. Recommending friends and locations based on individual location history[J]. ACM Transactions on the Web,2011,5(1):No. 5. [4] ZHENG Y,ZHANG L,XIE X,et al. Mining interesting locations and travel sequences from GPS trajectories[C]//Proceedings of the 18th International Conference on World Wide Web. New York:ACM,2009:791-800. [5] ZHENG V W,ZHENG Y,XIE X,et al. Collaborative location and activity recommendations with GPS history data[C]//Proceedings of the 19th International Conference on World Wide Web. New York:ACM,2010:1029-1038. [6] CUI G,LUO J,WANG X. Personalized travel route recommendation using collaborative filtering based on GPS trajectories[J]. International Journal of Digital Earth,2018,11(3):284-307. [7] JIANG S,QIAN X,SHEN J,et al. Author topic model-based collaborative filtering for personalized POI recommendations[J]. IEEE Transactions on Multimedia,2015,17(6):907-918. [8] WEI L Y,ZHENG Y,PENG W C. Constructing popular routes from uncertain trajectories[C]//Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM,2012:195-203. [9] 刘艳平, 保继刚, 黄应淮, 等. 基于GPS数据的自驾车游客时空行为研究——以西藏为例[J]. 世界地理研究,2019,28(1):149-160. (LIU Y P, BAO J G, HUANG Y H, et al. Study on spatio-temporal behaviors of self-driving tourists based on GPS data:a case study of Tibet[J]. World Regional Studies,2019,28(1):149-160.) [10] ZHENG Y. Trajectory data mining:an overview[J]. ACM Transactions on Intelligent Systems and Technology,2015,6(3):No. 29. [11] LEE J G, HAN J, WHANG K Y. Trajectory clustering:a partition-and-group framework[C]//Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data. New York:ACM,2007:593-604. [12] TANG W,PI D,HE Y. A density-based clustering algorithm with sampling for travel behavior analysis[C]//Proceedings of the 2016 International Conference on Intelligent Data Engineering and Automated Learning,LNCS 9937. Cham:Springer,2016:231-239. [13] BESSE P C,GUILLOUET B,LOUBES J M,et al. Review and perspective for distance-based clustering of vehicle trajectories[J]. IEEE Transactions on Intelligent Transportation Systems, 2016,17(11):3306-3317. [14] YI B K,JAGADISH H V,FALOUTSOS C. Efficient retrieval of similar time sequences under time warping[C]//Proceedings of the 14th International Conference on Data Engineering. Piscataway:IEEE,1998:201-208. [15] VLACHOS M,KOLLIOS G,GUNOPOULOS D. Discovering similar multidimensional trajectories[C]//Proceedings of the 18th International Conference on Data Engineering. Piscataway:IEEE, 2002:673-684. [16] CHEN L,ÖZSU M T,ORIA V. Robust and fast similarity search for moving object trajectories[C]//Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data. New York:ACM,2005:491-502. [17] WANG J,HUANG P,ZHAO H,et al. Billion-scale commodity embedding for e-commerce recommendation in Alibaba[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM,2018:839-848. [18] KENTHAPADI K,LE B,VENKATARAMAN G. Personalized job recommendation system at LinkedIn:practical challenges and lessons learned[C]//Proceedings of the 11th ACM Conference on Recommender Systems. New York:ACM,2017:346-347. [19] RADOSAVLJEVIC V,GRBOVIC M,DJURIC N,et al. Smartphone app categorization for interest targeting in advertising marketplace[C]//Proceedings of the 25th International Conference Companion on World Wide Web. New York:ACM,2016:93-94. [20] GRBOVIC M, CHENG H. Real-time personalization using embeddings for search ranking at Airbnb[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM,2018:311-320. [21] PEROZZI B, AL-RFOU R, SKIENA S. DeepWalk:online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM,2014:701-710. [22] WIETING J,BANSAL M,GIMPEL K,et al. Towards universal paraphrastic sentence embeddings[EB/OL].[2019-09-20]. https://arxiv.org/pdf/1511.08198.pdf. |