As recommendation systems become increasingly prevalent in numerous fields, the need for clear and persuasive interactions with users is rising. Integrating explainability into these systems is emerging as an effective approach to enhance user trust and sociability. This research focuses on recommendation systems that utilize a range of explainability techniques to foster trust by providing understandable personalized explanations for the recommendations made. In line with this, we study three distinct explanation methods that correspond with three basic recommendation strategies and assess their efficacy through user experiments. The findings from the experiments indicate that the majority of participants value the suggested explanation styles and favor straightforward, concise explanations over comparative ones.