Abstract
In the following we will describe systematically and formally the most important problems related to recommender systems and give some references to actual solutions. Our focus here is to describe the general recommender systems setting as a base for social recommender systems. See [11, 3] for a more general introduction to recommender systems and a more thorough overview of the state-of-the-art, respectively.
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References
G. Adomavicius and Y. O Kwon. New recommendation techniques for multicriteria rating systems. IEEE Intelligent Systems, page 48–55, 2007.
G. Adomavicius and A. Tuzhilin. Multidimensional recommender systems: a data warehousing approach. Electronic Commerce, page 180–192, 2001.
G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of the-art and possible extensions. IEEE transactions on knowledge and data engineering, page 734–749, 2005.
G. Adomavicius and A. Tuzhilin. Context-aware recommender systems. In Proceedings of the 2008 ACM conference on Recommender systems RecSys 08, volume 16, 2008.
M. Balabanović and Y. Shoham. Fab: content-based, collaborative recommendation. Communications of the ACM, 40(3):66–72, 1997.
E. J Candès and B. Recht. Exact matrix completion via convex optimization. Foundations of Computational Mathematics, 9(6):717–772, 2009.
D. Goldberg, D. Nichols, B. M Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12):61–70, 1992.
A. S Harpale and Y. Yang. Personalized active learning for collaborative filtering. In Proceedings of the 31 st annual international ACM SIGIR conference on Research and development in information retrieval, page 91–98, 2008.
T. Hofmann and J. Puzicha. Latent class models for collaborative filtering. In International Joint Conference on Artificial Intelligence, volume 16, page 688–693, 1999.
Thomas Hofmann. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems, 22(1):89–115, January 2004.
Dietmar Jannach, Markus Zanker, Alexander Felfernig, and Gerhard Friedich. Recommender Systems: An Introduction. Cambridge University Press, 1 edition, September 2010.
A. Karatzoglou, X. Amatriain, L. Baltrunas, and N. Oliver. Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In Proceedings of the fourth ACM conference on Recommender systems, page 79–86, 2010.
Yehuda Koren, Robert Bell, and Chris Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):37, 30, 2009.
C. Lippert, S. H Weber, Y. Huang, V. Tresp, M. Schubert, and H. P Kriegel. Relation-prediction in multi relational domains using matrixfactorization. In NIPS Workshop: Structured Input-Structured Output, 2008.
Ulrich Paquet, Blaise Thomson, and Ole Winther. A hierarchical model for ordinal matrix factorization. Statistics and Computing, June 2011.
I. Pilászy and D. Tikk. Recommending new movies: even a few ratings are more valuable than metadata. In Proceedings of the third ACM conference on Recommender systems, page 93–100, 2009.
A. M Rashid, I. Albert, D. Cosley, S. K Lam, S. M McNee, J. A Konstan, and J. Riedl. Getting to know you: learning new user preferences in recommender systems. In Proceedings of the 7th international conference on Intelligent user interfaces, page 127–134, 2002.
Steffen Rendle. Context-Aware Ranking with Factorization Models. Springer Berlin Heidelberg, 1st edition, November 2010.
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Schmidt- Thieme Lars. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty
in Artificial Intelligence, UAI ’09, pages 452–461, Arlington, Virginia, United States, 2009. AUAI Press.
P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. GroupLens: an open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work, page 175–186, 1994.
A. I Schein, A. Popescul, L. H Ungar, and D. M Pennock. Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, page 253–260, 2002.
L. Schmidt-Thieme. Compound classification models for recommender systems. In Fifth IEEE International Conference on Data Mining (ICDM’05), pages 378–385, Houston, TX, USA, 2005.
U. Shardanand and P. Maes. Social information filtering: algorithms for automating “word of mouth”. In Proceedings of the SIGCHI conference on Human factors in computing systems, page 210–217, 1995.
Ajit P. Singh and Geoffrey J. Gordon. Relational learning via collective matrix factorization. In Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 650–658, Las Vegas, Nevada, USA, 2008. ACM
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Marinho, L.B. et al. (2012). Recommender Systems. In: Recommender Systems for Social Tagging Systems. SpringerBriefs in Electrical and Computer Engineering(). Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-1894-8_2
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DOI: https://doi.org/10.1007/978-1-4614-1894-8_2
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