  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.
