This paper explores the use of ChatGPT to provide automated, human-centered explanations for recommendations in recommender systems (RSs). The authors address the lack of transparency and interpretability in RSs by leveraging the conversational capabilities of ChatGPT to generate personalized and meaningful explanations. The study includes a user study with 94 participants to evaluate the effectiveness, personalization, and persuasiveness of ChatGPT-generated explanations compared to generic ones. Key findings include: 1. **Recommendation Satisfaction**: Participants preferred ChatGPT-generated recommendations over random but popular recommendations, regardless of the movie's familiarity. 2. **User-based vs. Generic Explanations**: User-based explanations were not perceived as more personalized or effective than generic explanations, except for random recommendations. 3. **Movie Familiarity**: User-based explanations were more effective for unfamiliar movies, while generic explanations were more effective for familiar movies. 4. **Path Modeling**: Explanation effectiveness was predicted by satisfaction, persuasiveness, and personalization, with persuasiveness being the strongest predictor. The study highlights the importance of personalized explanations in enhancing user satisfaction and suggests that future research should focus on larger samples and additional explanation goals to better understand user perceptions.This paper explores the use of ChatGPT to provide automated, human-centered explanations for recommendations in recommender systems (RSs). The authors address the lack of transparency and interpretability in RSs by leveraging the conversational capabilities of ChatGPT to generate personalized and meaningful explanations. The study includes a user study with 94 participants to evaluate the effectiveness, personalization, and persuasiveness of ChatGPT-generated explanations compared to generic ones. Key findings include: 1. **Recommendation Satisfaction**: Participants preferred ChatGPT-generated recommendations over random but popular recommendations, regardless of the movie's familiarity. 2. **User-based vs. Generic Explanations**: User-based explanations were not perceived as more personalized or effective than generic explanations, except for random recommendations. 3. **Movie Familiarity**: User-based explanations were more effective for unfamiliar movies, while generic explanations were more effective for familiar movies. 4. **Path Modeling**: Explanation effectiveness was predicted by satisfaction, persuasiveness, and personalization, with persuasiveness being the strongest predictor. The study highlights the importance of personalized explanations in enhancing user satisfaction and suggests that future research should focus on larger samples and additional explanation goals to better understand user perceptions.