Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Recommended Reading
Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng TKDE. 2005;17(6):734–49.
Breese JS, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence; 1998.
Burke R. Hybrid recommender systems: survey and experiments. User Model User-Adap Inter. 2002;12(4):331–70.
Das A, Datar M, Garg A, Rajaram S. Google news personalization: scalable online collaborative filtering. In: Proceedings of the 16th International World Wide Web Conference; 2007.
Koren Y, Bell RM, Volinsky C. Matrix factorization techniques for recommender systems. IEEE Comput. 2009;42(8):30–7.
Levandoski JJ, Sarwat M, Mokbel MF, Ekstrand MD. RecStore: an extensible and adaptive framework for online recommender queries inside the database engine. In: Proceedings of the 15th International Conference on Extending Database Technology; 2012.
Lops P, de Gemmis M, Semeraro G. Content-based recommender systems: state of the art and trends. In: Recommender systems handbook. Springer; 2011. p. 73–105. https://link.springer.com/book/10.1007/978-0-387-85820-3
Miller BN, Alber I, Lam SK, Konstan JA, Riedl J. MovieLens unplugged: experiences with an occasionally connected recommender system. In: Proceedings of the International Conference on Intelligent User Interfaces; 2002.
Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J. GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the ACM Conference on Computer Supported Cooperative Work; 1994.
Sarwar B, Karypis G, Konstan J, Riedl J. Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International World Wide Web Conference; 2001.
Sarwat M, Avery J, Mokbel MF. RecDB in action: recommendation made easy in relational databases. Proc. VLDB Endow. 2013;6(12):1242–5.
Sarwat M, Levandoski JJ, Eldawy A, Mokbel MF. LARS*: an efficient and scalable location-aware recommender system. IEEE Trans Knowl Data Eng. 2014;26(6):1384–99.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media, LLC, part of Springer Nature
About this entry
Cite this entry
Sarwat, M., Mokbel, M.F. (2018). Recommender Systems. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_80732
Download citation
DOI: https://doi.org/10.1007/978-1-4614-8265-9_80732
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-8266-6
Online ISBN: 978-1-4614-8265-9
eBook Packages: Computer ScienceReference Module Computer Science and Engineering