This is a preview of subscription content, log in via an institution to check access.
References
Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17:734–749
Agarwal D, Chen BC (2009) Regression-based latent factor models. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, New York, NY, USA, KDD ‘09, pp 19–28, DOI 10.1145/1557019.1557029., URL http://doi.acm.org/10.1145/1557019.1557029
Agarwal D, Chen BC, Elango P (2009) Spatio-temporal models for estimating click-through rate. In: Proceedings of the 18th international conference on World wide web, ACM, New York, NY, USA, WWW ‘09, pp 21–30, DOI 10.1145/1526709.1526713., URL http://doi.acm.org/10.1145/1526709.1526713
Agarwal D, Chen BC, Elango P (2010) Fast online learning through offline initialization for time-sensitive recommendation. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, New York, NY, USA, KDD ‘10, pp 703–712, DOI 10.1145/1835804.1835894., URL http://doi.acm.org/10.1145/1835804.1835894
Agarwal D, Chen BC, Elango P, Wang X (2011a) Click shaping to optimize multiple objectives. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, New York, NY, USA, KDD ‘11, pp 132–140, DOI 10.1145/2020408.2020435., URL http://doi.acm.org/10.1145/2020408.2020435
Agarwal D, Chen BC, Long B (2011b) Localized factor models for multi-context recommendation. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, New York, NY, USA, KDD ‘11, pp 609–617, DOI 10.1145/2020408.2020504., URL http://doi.acm.org/10.1145/2020408.2020504
Agrawal R, Imielin’ski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD international conference on Management of data, ACM, New York, NY, USA, SIGMOD ‘93, pp 207–216, DOI 10.1145/170035.170072., URL http://doi.acm.org/10.1145/170035.170072
Ahmed A, Low Y, Aly M, Josifovski V, Smola AJ (2011) Scalable distributed inference of dynamic user interests for behavioral targeting. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, New York, NY, USA, KDD ‘11, pp 114–122, DOI 10.1145/2020408.2020433., URL http://doi.acm.org/10.1145/2020408.2020433
Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022. URL http://dl.acm.org/citation.cfm?id=944919.944937
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Springer, New York
Hu B, Jamali M, Ester M (2013) Spatio-temporal topic modeling in mobile social media for location recommendation. In: 2013 I.E. 13th International Conference on Data Mining, IEEE, pp 1073–1078
Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the fourth ACM conference on Recommender systems, ACM, New York, NY, USA, RecSys ‘10, pp 135–142, DOI 10.1145/1864708.1864736., URL http://doi.acm.org/10.1145/1864708.1864736
Jannach D, Zanker M, Felfernig A, Friedrich G (2010) Recommender systems: an introduction. Cambridge University Press, New York. URL http://books.google.com/books?id=eygTJBd U2cC
Kaplan AM, Haenlein M (2010) Users of the world, unite! the challenges and opportunities of social media. Bus Horiz 53(1):59–68. doi:10.1016/j.bushor.2009.09.003. URL http://www.sciencedirect.com/science/article/pii/S0007681309001232
Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37
Ricci F, Rokach L, Shapira B, Kantor PB (eds) (2015) Recommender systems handbook. Springer, New York. http://www.springer.com/us/book/9781489976369
Salton G, Wong A, Yang CS (1975) A vector space model for automatic indexing. Commun ACM 18(11):613–620. doi:10.1145/361219.361220. URL http://doi.acm.org/10.1145/361219.361220
Xiong L, Chen X, Huang TK, Schneider JG, Carbonell JG (2010) Temporal collaborative filtering with bayesian probabilistic tensor factorization. In: Proceedings of the SIAM International Conference on Data Mining, SDM 2010, April 29–May 1, 2010, Columbus, Ohio, USA, pp 211–222
Yin H, Cui B (2016) Spatio-temporal recommendation in Social Media. Springer, Singapore. http://www.springer.com/us/book/9789811007477
Yuan Q, Cong G, Zhao K, Ma Z, Sun A (2015) Who, where, when, and what: a nonparametric bayesian approach to context-aware recommendation and search for twitter users. ACM Trans Inf Syst (TOIS) 33(1):2
Zheng VW, Cao B, Zheng Y, Xie X, Yang Q (2010) Collaborative filtering meets mobile recommendation: a user-centered approach. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence, pp 236–241
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media LLC
About this entry
Cite this entry
Chen, BC. (2017). Spatiotemporal Personalized Recommendation of Social Media Content. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7163-9_325-1
Download citation
DOI: https://doi.org/10.1007/978-1-4614-7163-9_325-1
Received:
Accepted:
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-7163-9
Online ISBN: 978-1-4614-7163-9
eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering