Optimizing Scalable Data-Driven Recommendations with Evolutionary Algorithms for Predictive Analytics

Evolutionary algorithms are optimization techniques inspired by the natural selection processes, which refine candidate solutions iteratively by selection, crossover, and mutation. This kind of algorithm is particular to solving complex problems with large search spaces, and they are often used in domains like data-driven e-commerce product recommendation systems. Within this paper, an innovative evolutionary algorithm is proposed to optimize scalable, data-driven recommendations within e-commerce systems. The most important aspect is the efficient management of large datasets, which is the hallmark of contemporary e-commerce platforms. The algorithm begins with an initialization of a diverse population of candidate solutions, which refers to different product recommendations based on a user's preferences. A sound fitness function is utilized to ascertain the goodness of candidate recommendations through the evaluation criteria of engagement and accuracy founded upon historical interaction data. The proposed evolutionary algorithm performs significantly better as it achieved accuracy with a precision of 98.9% at 97%, recall at 99%, and with an F1-Score of 98.3 %. The evolutionary search is a sequence of iterated cycles applying selection, crossover, and mutation operations to progressively refine the candidate solutions during such a process. Selection has the choices with the higher fitness values as preferred candidates.