Abstract
This paper presents preliminary results of using grammatical evolution to evolve expressions that calculate the user/item features used in the matrix factorization recommendation algorithm. The experiment was performed primarily to determine whether grammatical evolution can be applied to this field, and to compare the results with those of the ’traditional’ algorithm. For the purpose of the experiment, we used the CoMoDa dataset, which features realistic data collected over five years. The preliminary results are promising and offer a lot of possible future work, some of which is discussed at the end of the paper.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bennett, J., Lanning, S.: The netflix prize. In: Proceedings of KDD Cup and Workshop, vol. 2007, p. 35 (2007)
Benson, A.R., Lee, J.D., Rajwa, B., Gleich, D.F.: Scalable methods for nonnegative matrix factorizations of near-separable tall-and-skinny matrices. In: Advances in Neural Information Processing Systems, pp. 945–953 (2014)
Harper, R., Blair, A.: A structure preserving crossover in grammatical evolution. In: Corne, D., et al. (eds.) Proceedings of the 2005 IEEE Congress on Evolutionary Computation, vol. 3, pp. 2537–2544. IEEE Press (2005)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Košir, A., Odic, A., Kunaver, M., Tkalcic, M., Tasic, J.F.: Database for contextual personalization. Elektrotehniški vestnik 78(5), 270–274 (2011)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Kunaver, M., Košir, A., Tasič, J.F.: Hybrid recommender for multimedia item recommendation: development of a hybrid content-collaborative recommender system for multimedia item recommendation. LAP Lambert Academic Publishing (2011)
Liu, C., Yang, H.C., Fan, J., He, L., Wang, Y.M.: Distributed non-negativematrix factorization, 15 January 2013, uS Patent 8,356,086
Mnih, A., Salakhutdinov, R.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, pp. 1257–1264 (2007)
Odić, A., Tkalčič, M., Tasič, J.F., Košir, A.: Predicting and detecting the relevant contextual information in a movie-recommender system. Interact. Comput. 25, 74–90 (2013)
O’Neill, M., Ryan, C.: Grammatical evolution. IEEE Trans. Evol. Comput. 5(4), 349–358 (2001)
Ryan, C., Azad, R.M.A.: Sensible initialisation in chorus. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 394–403. Springer, Heidelberg (2003)
Takács, G., Pilászy, I., Németh, B., Tikk, D.: Matrix factorization and neighbor based algorithms for the netflix prize problem. In: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 267–274. ACM (2008)
Acknowledgments
This work was supported by the Ministry of Education, Science and Sport of Republic of Slovenia under Research program P2-0246 - Algorithms and optimization methods in telecommunications.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Kunaver, M., Fajfar, I. (2016). Grammatical Evolution in a Matrix Factorization Recommender System. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9692. Springer, Cham. https://doi.org/10.1007/978-3-319-39378-0_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-39378-0_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-39377-3
Online ISBN: 978-3-319-39378-0
eBook Packages: Computer ScienceComputer Science (R0)