This paper presents a probabilistic-temporal modeling approach for analyzing data processing stages in a personalized recommendation system for digital heritage collections. The methodology is based on (Graphical Evaluation and Review Technique) GERT network formalism, which enables the representation of complex probabilistic workflows with feedbacks and alternative branches. For each processing stage, corresponding GERT-schemes were developed, and equivalent transfer functions were derived. Using Laplace transform inversion techniques, probability density functions of processing time were recovered, followed by the calculation of key statistical metrics, including expectation, standard deviation, and quantiles. The results demonstrate that the proposed approach allows for detailed temporal performance evaluation, including the estimation of time exceedance probabilities at each stage. This provides a quantitative basis for optimizing recommendation system design and highlights the applicability of GERT-based modeling to intelligent data-driven services in the cultural domain.