  Recommender system is an applicable technique in most E-commerce commercial
product technical designs. However, nearly all recommender system faces a
challenge called the cold-start problem. The problem is so notorious that
almost every industrial practitioner needs to resolve this issue when building
recommender systems. Most cold-start problem solvers need some kind of data
input as the starter of the system. On the other hand, many real-world
applications place popular items or random items as recommendation results. In
this paper, we propose a new technique called ZeroMat that requries no input
data at all and predicts the user item rating data that is competitive in Mean
Absolute Error and fairness metric compared with the classic matrix
factorization with affluent data, and much better performance than random
placement.
