Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 191-197.doi: 10.11896/jsjkx.201200015
• Big Data & Data Science • Previous Articles Next Articles
LI Ai-ling, ZHANG Feng-li, GAO Qiang, WANG Rui-jin
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[1]LIAN D F,ZHAO C,XIE X,et al.GeoMF:Joint Geographical Modeling and Matrix Factorization for Point-of-Interest Recommendation[C]//Acm Sigkdd International Conference on Knowledge Discovery & Data Mining.ACM,2014. [2]GAMBS S,KILLIJIAN M O,DEL PRADO CORTEZ M N.Next Place Prediction Using Mobility Markov Chains[C]//MPM.2012. [3]ASAHARA A,SATO A,MARUYAMA K,et al.Pedestrian-movement prediction based on mixed Markov-chain model[C]//Proceedings of the 19th International Conference on Advances in Geographic Information Systems.IL,USA,2011:25-33. [4]ASHBROOK D,STARNER T.Learning significant locationsand predicting user movement with GPS[C]//Proceedings of the 6th International Symposium on Wearable Computers.Sardina,Italy,2003:275-286. [5]VITTER J S,KRISHNAN P.Optimal prefetching via data compression[J].Journal of the ACM,NY,USA,1996,43:771-793. [6]ZHENG W C,CAO B,ZHENG Y,et al.Collaborative Filtering Meets Mobile Recommendation:A User-centered Approach[C]//AAAI.2010. [7]FENG S S,LI X T,ZENG Y F,et al.Personalized ranking metric embedding for next new POI recommendation[C]//International Conference on Artificial Intelligence.AAAI Press,2015. [8]CHENG C,YANG H Q,LYU M R,et al.Where You Like to Go Next:Successive Point-of-Interest Recommendation[C]//IJCAI.2013. [9]LI X T,CONG G,LI X L,et al.Rank-GeoFM:A Ranking BasedGeographical Factorization Method for Point of Interest Recommendation[C]//SIGIR.2015. [10]RENDLE S,FREUDENTHALER C,SCHMIDT-THIEME L.Factorizing personalized Markov chains for next-basket recommendation[C]//Proceedings of the 19th International Conference on World Wide Web(WWW 2010).Raleigh,North Carolina,USA:ACM,2010:26-30. [11]MIKOLOV T,CHEN K,CORRADO G,et al.Efficient Estimation of Word Representations in Vector Space[C]//ICLR 2013.2013. [12]FENG S S,CONG G,AN B,et al.POI2Vec:Geographical Latent Representation for Predicting Future Visitors[C]//AAAI.2017. [13]LIU Q,WU S,WANG L,et al.Predicting the Next Location:A Recurrent Model with Spatial and Temporal Contexts[C]//Thirtieth Aaai Conference on Artificial Intelligence.AAAI Press,2016. [14]FENG J,LI Y,ZHANG C,et al.DeepMove:Predicting Human Mobility with Attentional Recurrent Networks[C]//WWW.2018. [15]GAO Q,ZHOU F,TRAJCEVSKI G,et al.Predicting HumanMobility via Variational Attention[C]//WWW.2019. [16]ZHOU F,GAO Q,ZHANG K P,et al.Trajectory-User Linking via Variational AutoEncoder[C]//IJCAI.2018. [17]YUAN J,ZHENG Y,ZHANG L,et al.Where to Find My Next Passenger?[C]//UbiComp 2011:Ubiquitous Computing,13th International Conference(UbiComp 2011).Beijing,China,2011:17-21. [18]YUAN,JING N,ZHENG,et al.1 T-Finder:A RecommenderSystem for Finding Passengers and Vacant Taxis[J].IEEE Transactions on Knowledge and Data Engineering,2012,25(10):2390-2403. [19]ZHENG Y,ZHANG L Z,XIE X,et al.Mining interesting locations and travel sequences from GPS trajectories[C]//WWW.2009. [20]BAO J,ZHENG Y,MOKBEL M F.Location-based and prefe-rence-aware recommendation using sparse geosocial networkingdata[C]//ACM SIGSPATIAL GIS.2012. [21]ZHENG Y,ZHANG L Z,XIE X,et al.Mining correlation between locations using human location history[C]//ACM SIGSPATIAL GIS.2009. [22]ZHENG Y,XIE X.Learning location correlation from GPS trajectories[C]//MDM.2010. [23]ZHENG Y,XIE X.Learning travel recommendations from user-generated GPS traces[J].Acm Transactions on Intelligent Systems & Technology,2011,2(1):1-29. [24]GAO Q,ZHANG F,YAO F,et al.Adversarial Mobility Learning for Human Trajectory Classification[J].IEEE Access,2020,PP(99):1-1. [25]YANG D Q,ZHANG D Q,ZHENG V W,et al.Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2015,45(1):129-142. [26]GAO Q,ZHANG F L,WANG R J,et al.Trajectory Big Data:A Review of Key Technologies in Data Processing Summary[J].Ruan Jian Xue Bao/Journal of Software,2017,28(4):959-992. [27]XU J J,ZHENG K,CHI M M,et al.Trajectory big data:data,application and technology status[J].Journal on Communications,2015,36(12):97-105. [28]MAO J L,JIN C Q,ZHANG Z G,et al.Anomaly Detection for Trajectory big data:Advancements and Framework[J].Ruan Jian Xue Bao,2017,28(1):17-34. [29]MENG X W,LI R C,ZHANG Y J,et al.Survery on mobile rec-ommender systems based on user trajectory data[J].Ruan Jian Xue Bao,2018,29(10):3111-3133. [30]PENG H W,JIN Y Y,LU X Q,et al.Context-aware POI Recommendation Based on Matrix Factorization [J].Chinese Journal of Computers,2019(8):1797-1811. [31]ZHANG J L,SHI H L,CUI L.Location Prediction Model Based on Transportation Mode and Semantic Trajectory[J].Computer Research and Development,2019,56(7):1357-1369. |
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