Abstract
This article presents the application of the differential evolution algorithm (DE), the aim of which is to adjust weights to particular features of an active users profile, in order to render his preferences in the best possible way. In our system we applied a popular technique of collaborative filtering, which is used to generate recommendations. The users profile is a vector, which consists of values, which characterize a given user. Using a hybrid feature made it possible to use simple weighted Euclidean distance, which significantly decreased the amount of necessary computations and made it possible to compare particular profiles in a system faster. The results of the conducted experiments were compared with a modified weighted Euclidean distance and Pearsons correlation.
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Bałchanowski, M., Boryczka, U., Dworak, K. (2018). Using Differential Evolution with a Simple Hybrid Feature for Personalized Recommendation. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science(), vol 10751. Springer, Cham. https://doi.org/10.1007/978-3-319-75417-8_13
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