The problems of recommender systems (RS) improvement were considered in the context of big data (BD) processing in information and communication systems. The popularity of this topic is due to the need to develop high-performance personalization algorithms among the growing volumes of data used by e-commerce, education, healthcare, and live broadcast platforms. The challenges associated with the scalability and efficiency of big data processing require innovative approaches to ensure sustainable development. The paper proposes two modified versions of the Funk SVD algorithm. The first method is based on optimizing the latent factor matrix using gradient descent (GD), which reduces computation time. The second approach integrates additional functionality by normalizing the input data and applying gradient boosting (GB) to the regressors, which allows the computation of complex dependencies between users and objects. Both approaches aim to increase the speed and scalability of recommendation algorithms. Experimental results show that the first modification of Funk SVD exhibits a slight decrease in accuracy but works 22.9% faster than the standard method. The second modification takes into account complex data, reducing execution time by 50.38%. Experimental and graphical results confirm that the proposed algorithms significantly reduce computation time and maintain stable performance even on large data sets. In particular, the second modification has better scalability due to the normalization of functions and the use of gradient boosting. The developed methods are of important practical significance, as they provide a balance between accuracy, speed, and efficiency. This makes them suitable for scalable recommender systems, especially when speed and processing large amounts of data are critical, as in the case of streaming services and marketing platforms. By addressing the efficiency of resource utilization and enabling scalable solutions, the results contribute to the sustainable development of digital infrastructure across diverse sectors.