Recommender system exists everywhere in the business world. From Goodreads to TikTok, customers of internet products become more addicted to the products thanks to the technology. Industrial practitioners focus on increasing the technical accuracy of recommender systems while at same time balancing other factors such as diversity and serendipity. In spite of the length of the research and development history of recommender systems, there has been little discussion on how to take advantage of visualization techniques to facilitate the algorithmic design of the technology. In this paper, we use a series of data analysis and visualization techniques such as Takens Embedding, Determinantal Point Process and Social Network Analysis to help people develop effective recommender systems by predicting intermediate computational cost and output performance. Our work is pioneering in the field, as to our limited knowledge, there have been few publications (if any) on visualization of recommender systems.

翻译：推荐系统在商业世界中无处不在。从Goodreads到TikTok，得益于该技术，互联网产品的用户对产品愈发沉迷。工业实践者专注于提升推荐系统的技术精度，同时兼顾多样性、偶然性等其他因素。尽管推荐系统的研究与开发已有较长历史，但关于如何利用可视化技术辅助该技术的算法设计却鲜有讨论。本文通过一系列数据分析与可视化技术（如Takens嵌入、行列式点过程与社会网络分析），预测中间计算成本与输出性能，从而帮助人们开发有效的推荐系统。据我们所知，此前鲜有（甚至没有）关于推荐系统可视化的文献发表，因此本研究在该领域具有开创性意义。