：亚马逊网站为每本图书生成推荐列表，供用户选择其感兴趣的图书，但该推荐方法仅考虑图书之间的相似性。文章在此基础上引入图书推荐列表的相似性，计算不同图书推荐列表的相似度，通过相似度排序为每本图书生成推荐列表，然后将其与亚马逊提供的推荐列表融合对用户进行个性化图书推荐。实验结果显示，相比于仅使用推荐列表的方法，文章所提出的方法所生成的推荐结果在平均准确率、平均召回率、Macro_F1和Micro_F1上都有一定的提升。由此可见，推荐列表的相似性对于图书推荐效果可以起到一定的优化作用。

The Amazon website generates recommendation lists for each book, allowing users to select the books they are interested in. But this recommendation method only considers the similarity among books. Based on the recommendation lists, this paper introduces a similarity of book recommendation lists, and calculates Jaccard similarity of different book recommendation lists, then generates recommendation list for each book by similarity ranking. This paper combines it and the recommendation list provided by Amazon to conduct personalized recommendation for users. The experimental results show that there is a certain improvement in the average accuracy rate, the average recall rate, Macro_F1 and Micro_F1 compared to using only the recommendation list provided by Amazon. It can be seen that the similarity of the recommendation list of the book can play a certain role in optimizing the recommendation effect.