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.
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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.
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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
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DOI: https://doi.org/10.1007/978-3-319-39378-0_34
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