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.

我们研究了现代模型生成的电影推荐的文本解释如何帮助用户，以及探讨用户对这些文本解释的不同组成部分的喜好和厌恶，特别是与现有的人类电影评论相比。我们发现，参与者对电影之间的排名以及对从未看过的电影评论的个人质量评分没有显著差异。然而，当评论是关于他们之前看过的电影时，参与者标记为更好的评论。此外，我们还探讨了参与者认为每个质量重要的电影评论文本的具体方面。总的来说，我们证明了现代 LLMs 是有希望作为推荐解释的来源，我们打算在未来进一步探索可个性化的文本解释。