In this work, we establish a baseline potential for how modern model-generated text explanations of movie recommendations may help users, and explore what different components of these text explanations that users like or dislike, especially in contrast to existing human movie reviews. We found that participants gave no significantly different rankings between movies, nor did they give significantly different individual quality scores to reviews of movies that they had never seen before. However, participants did mark reviews as significantly better when they were movies they had seen before. We also explore specific aspects of movie review texts that participants marked as important for each quality. Overall, we establish that modern LLMs are a promising source of recommendation explanations, and we intend on further exploring personalizable text explanations in the future.

翻译：在本研究中，我们建立了现代模型生成的电影推荐文本解释可能帮助用户的基线潜力，并探讨了这些文本解释中用户喜欢或不喜欢的组成部分，特别是与现有的人类电影评论相比。我们发现，参与者对电影的评分排名没有显著差异，对于他们从未看过的电影的评论，也没有给出显著不同的个体质量评分。然而，当电影是他们曾经看过的时，参与者对评论的评分显著更高。我们还探讨了电影评论文本中参与者认为对每种质量重要的具体方面。总体而言，我们确定了现代LLM是推荐解释的有前景来源，并计划未来进一步探索可个性化定制的文本解释。