Exploring Personality-Aware Explanations for Recommender Systems

Recommender systems guide users through vast amounts of data, yet recommendations can feel opaque and difficult to trust. Prior research shows that explanations help users become more confident and satisfied, and that explanation effectiveness depends not only on what is said but also on how it is communicated. This work investigates personality-aligned framings of explanations in a music recommender system. Using generative AI, we produce natural language explanations aligned with the Big Five personality traits alongside a feature-based baseline and examine whether linguistic framing shapes user perceptions beyond personalization. In a controlled study with N = 348 participants, users completed a Big Five inventory and evaluated music recommendations presented with six framing conditions. Results show that linguistic framing influences how users perceive recommendations, with some framings perceived as more persuasive, clearer, or more accurate than others. Effects vary across personality traits, but aligning framing with a user’s dominant trait does not consistently improve perceptions, while emotionally stable framings are broadly effective. These findings suggest that linguistic framing is a meaningful design parameter for recommender explanations, and that domain fit and emotional appropriateness, rather than user-trait matching, are the operative factors. We discuss implications for adaptive explanation design using generative AI.