Exploration and exploitation (E&E) of a search space are two fundamental processes in many fields of artificial intelligence. Indeed, when the search space is vast, it is important to ensure that many of its regions are examined, so as not to get trapped in a local optimum, but also that promising regions are examined more in depth in order to find good local optima. Influencing both processes is thus necessary. The literature has rarely proposed to approach the recommendation task as a vast search space problem. This paper introduces a metaheuristic-based recommendation approach with two contributions: an E&E influence process which is independent from the influenced algorithm and new indicators to represent and explain E&E. Performed on a genetic algorithm (GA) and on a reinforcement algorithm (RA), our experiments confirm that (1) the proposed influence process has a positive impact on evaluation criteria and on E&E which brings better recommendations, (2) proposed indicators contribute to represent E&E from new angles.