Large language models (LLMs) are increasingly prevalent in recommender systems, where LLMs can be used to generate personalized recommendations. Here, we examine how different LLM-generated explanations for movie recommendations affect users' perceptions of cognitive, affective, and utilitarian needs and consumption intentions. In a pre-registered, between-subject online experiment (N=759) and follow-up interviews (N=30), we compare (a) LLM-generated generic explanations, and (b) LLM-generated contextualized explanations. Our findings show that contextualized explanations (i.e., explanations that incorporate users' past behaviors) effectively meet users' cognitive needs while increasing users' intentions to watch recommended movies. However, adding explanations offers limited benefits in meeting users' utilitarian and affective needs, raising concerns about the proper design and implications of LLM-generated explanations. Qualitative insights from interviews reveal that referencing users' past preferences enhances trust and understanding but can feel excessive if overused. Furthermore, users with more active and positive engagement with the recommender system and movie-watching get substantial gains from contextualized explanations. Overall, our research clarifies how LLM-generated recommendations influence users' motivations and behaviors, providing valuable insights for the future development of user-centric recommender systems, a key element in social media platforms and online ecosystems.

翻译：大语言模型在推荐系统中日益普及，可用于生成个性化推荐。本研究探讨了LLM生成的不同电影推荐解释如何影响用户对认知需求、情感需求和功利需求的感知以及消费意愿。通过一项预先注册的组间在线实验（N=759）和后续访谈（N=30），我们比较了（a）LLM生成的通用解释与（b）LLM生成的情境化解释。研究发现：情境化解释（即融合用户历史行为的解释）能有效满足用户的认知需求，同时提升用户观看推荐电影的意愿；然而，添加解释在满足用户功利需求和情感需求方面收效有限，这引发了对LLM生成解释的合理设计及其影响的思考。访谈定性分析表明：援引用户历史偏好能增强信任感与理解度，但过度使用会引发冗余感；此外，对推荐系统和观影行为参与更积极、态度更正向的用户能从情境化解释中获得显著收益。本研究系统阐释了LLM生成推荐如何影响用户动机与行为，为社交媒体平台及在线生态系统中关键组成部分——以用户为中心的推荐系统的未来发展提供了重要见解。