Modeling Spatio-Temporal App Usage for a Large User Population
暂无分享,去创建一个
Gang Wang | Pan Hui | Depeng Jin | Yong Li | Sihan Zeng | Pengyu Zhang | Huandong Wang | G. Wang | Pan Hui | Yong Li | Pengyu Zhang | Depeng Jin | Huandong Wang | Sihan Zeng
[1] Li Guo,et al. Location-aware Friend Recommendation in Event-based Social Networks: A Bayesian Latent Factor Approach , 2016, CIKM.
[2] Xiaoxiao Ma,et al. Predicting mobile application usage using contextual information , 2012, UbiComp.
[3] Vassilis Kostakos,et al. Revisitation in Urban Space vs. Online , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..
[4] Reza Shokri,et al. Predicting Users' Motivations behind Location Check-Ins and Utility Implications of Privacy Protection Mechanisms , 2015, NDSS.
[5] Xing Xie,et al. Discovering regions of different functions in a city using human mobility and POIs , 2012, KDD.
[6] Tat-Seng Chua,et al. Addressing cold-start in app recommendation: latent user models constructed from twitter followers , 2013, SIGIR.
[7] Alexandre Proutière,et al. Cluster-aided mobility predictions , 2015, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.
[8] Dino Pedreschi,et al. Human mobility, social ties, and link prediction , 2011, KDD.
[9] Nicholas R. Jennings,et al. Modelling heterogeneous location habits in human populations for location prediction under data sparsity , 2013, UbiComp.
[10] Alexandros Karatzoglou,et al. Climbing the app wall: enabling mobile app discovery through context-aware recommendations , 2012, CIKM '12.
[11] Anders Lindgren,et al. Prediction of user app usage behavior from geo-spatial data , 2016, GeoRich@SIGMOD.
[12] Deborah Estrin,et al. Diversity in smartphone usage , 2010, MobiSys '10.
[13] Ye Xu,et al. Preference, context and communities: a multi-faceted approach to predicting smartphone app usage patterns , 2013, ISWC '13.
[14] Lei Cen,et al. Personalized Mobile App Recommendation: Reconciling App Functionality and User Privacy Preference , 2015, WSDM.
[15] John Krumm. Realistic Driving Trips For Location Privacy , 2009, Pervasive.
[16] Ricardo Baeza-Yates,et al. Predicting The Next App That You Are Going To Use , 2015, WSDM.
[17] Ron Kohavi,et al. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.
[18] Michael McGill,et al. Introduction to Modern Information Retrieval , 1983 .
[19] Bruno Martins,et al. Predicting future locations with hidden Markov models , 2012, UbiComp.
[20] Li Su,et al. From Fingerprint to Footprint , 2019, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..
[21] Hojung Cha,et al. Mobility prediction-based smartphone energy optimization for everyday location monitoring , 2011, SenSys.
[22] Wen-Ning Kuo,et al. Urban point-of-interest recommendation by mining user check-in behaviors , 2012, UrbComp '12.
[23] LiLei,et al. Personalized news recommendation via implicit social experts , 2014 .
[24] Qiang Xu,et al. Identifying diverse usage behaviors of smartphone apps , 2011, IMC '11.
[25] Hui Xiong,et al. Popularity Modeling for Mobile Apps: A Sequential Approach , 2015, IEEE Transactions on Cybernetics.
[26] Pei-Yun Tsai,et al. Personalized recommendation of popular blog articles for mobile applications , 2011, Inf. Sci..
[27] J. Shewchuk. An Introduction to the Conjugate Gradient Method Without the Agonizing Pain , 1994 .
[28] Jean-François Paiement,et al. A Generative Model of Urban Activities from Cellular Data , 2018, IEEE Transactions on Intelligent Transportation Systems.
[29] Úlfar Erlingsson,et al. RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response , 2014, CCS.
[30] Yee Whye Teh,et al. Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes , 2004, NIPS.
[31] Liyuan Liu,et al. TrioVecEvent: Embedding-Based Online Local Event Detection in Geo-Tagged Tweet Streams , 2017, KDD.
[32] Anindya Datta,et al. Serendipitous Recommendation for Mobile Apps Using Item-Item Similarity Graph , 2013, AIRS.
[33] Yoshua Bengio,et al. Pattern Recognition and Neural Networks , 1995 .
[34] Zhaohui Wu,et al. Discovering different kinds of smartphone users through their application usage behaviors , 2016, UbiComp.
[35] David S. Rosenblum,et al. A Non-Parametric Generative Model for Human Trajectories , 2018, IJCAI.
[36] G. Chowdhury,et al. Introduction to Modern Information Retrieval, 3rd Edition , 2010 .
[37] Eric Horvitz,et al. Predestination: Inferring Destinations from Partial Trajectories , 2006, UbiComp.
[38] Masashi Morimoto,et al. Circular object detection based on separability and uniformity of feature distributions using Bhattacharyya Coefficient , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).
[39] Andrew McCallum,et al. Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression , 2008, UAI.
[40] Kamal Ali,et al. GetJar mobile application recommendations with very sparse datasets , 2012, KDD.
[41] Antonio Krüger,et al. AppFunnel: a framework for usage-centric evaluation of recommender systems that suggest mobile applications , 2013, IUI '13.
[42] F. D. Garber,et al. The Quality of Training Sample Estimates of the Bhattacharyya Coefficient , 1990, IEEE Trans. Pattern Anal. Mach. Intell..
[43] Dragomir Yankov,et al. Interoperability ranking for mobile applications , 2013, SIGIR.
[44] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[45] Chen Lin,et al. Personalized news recommendation via implicit social experts , 2014, Inf. Sci..
[46] Jure Leskovec,et al. Friendship and mobility: user movement in location-based social networks , 2011, KDD.
[47] Jie Liu,et al. Fast app launching for mobile devices using predictive user context , 2012, MobiSys '12.
[48] Hui Xiong,et al. A Survey of Context-Aware Mobile Recommendations , 2013, Int. J. Inf. Technol. Decis. Mak..
[49] Chao Huang,et al. Privacy-preserving Cross-domain Location Recommendation , 2019, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..
[50] Luming Zhang,et al. GMove: Group-Level Mobility Modeling Using Geo-Tagged Social Media , 2016, KDD.
[51] Albert-László Barabási,et al. Understanding individual human mobility patterns , 2008, Nature.
[52] T. Geisel,et al. The scaling laws of human travel , 2006, Nature.
[53] Wei Zhang,et al. PRED: Periodic Region Detection for Mobility Modeling of Social Media Users , 2017, WSDM.
[54] Gang Wang,et al. Your Apps Give You Away , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..
[55] Reza Shokri,et al. Synthesizing Plausible Privacy-Preserving Location Traces , 2016, 2016 IEEE Symposium on Security and Privacy (SP).
[56] Hui Xiong,et al. Mobile app recommendations with security and privacy awareness , 2014, KDD.
[57] Margaret Martonosi,et al. Human mobility modeling at metropolitan scales , 2012, MobiSys '12.
[58] Yong Liao,et al. SAMPLES: Self Adaptive Mining of Persistent LExical Snippets for Classifying Mobile Application Traffic , 2015, MobiCom.
[59] Aniket Kittur,et al. Bridging the gap between physical location and online social networks , 2010, UbiComp.
[60] Nadia Magnenat-Thalmann,et al. Time-aware point-of-interest recommendation , 2013, SIGIR.
[61] Ning Chen,et al. SimApp: A Framework for Detecting Similar Mobile Applications by Online Kernel Learning , 2015, WSDM.
[62] Aleksandar Kuzmanovic,et al. Measuring serendipity: connecting people, locations and interests in a mobile 3G network , 2009, IMC '09.
[63] Donghan Yu,et al. Smartphone App Usage Prediction Using Points of Interest , 2017, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..
[64] Hui Xiong,et al. Mining Personal Context-Aware Preferences for Mobile Users , 2012, 2012 IEEE 12th International Conference on Data Mining.
[65] Xing Xie,et al. Collaborative location and activity recommendations with GPS history data , 2010, WWW '10.
[66] Fei Yu,et al. Friend Recommendation Considering Preference Coverage in Location-Based Social Networks , 2017, PAKDD.
[67] Wang-Chien Lee,et al. App recommendation: a contest between satisfaction and temptation , 2013, WSDM.
[68] Nicholas Jing Yuan,et al. A contextual collaborative approach for app usage forecasting , 2016, UbiComp.
[69] Catuscia Palamidessi,et al. Geo-indistinguishability: differential privacy for location-based systems , 2012, CCS.
[70] Xin Lu,et al. Approaching the Limit of Predictability in Human Mobility , 2013, Scientific Reports.
[71] Yong Li,et al. Revealing Urban Dynamics by Learning Online and Offline Behaviours Together , 2019, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..
[72] Claude Castelluccia,et al. Study : Privacy Preserving Release of Spatio-temporal Density in Paris , 2014 .
[73] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[74] Alexander Markowetz,et al. Differentiating smartphone users by app usage , 2016, UbiComp.
[75] Mao Ye,et al. Location recommendation for location-based social networks , 2010, GIS '10.