  Recommender system is adored in the internet industry as one of the most
profitable technologies. Unlike other sectors such as fraud detection in the
Fintech industry, recommender system is both deep and broad. In recent years,
many researchers start to focus on the cold-start problem of recommender
systems. In spite of the large volume of research literature, the majority of
the research utilizes transfer learning / meta learning and pretrained model to
solve the problem. Although the researchers claim the effectiveness of the
approaches, everyone of them does rely on extra input data from other sources.
In 2021 and 2022, several zeroshot learning algorithm for recommender system
such as ZeroMat, DotMat, PoissonMat and PowerMat were invented. They are the
first batch of the algorithms that rely on no transfer learning or pretrained
models to tackle the problem. In this paper, we follow this line and invent a
new zeroshot learning algorithm named LogitMat. We take advantage of the Zipf
Law property of the user item rating values and logistic regression model to
tackle the cold-start problem and generate competitive results with other
competing techniques. We prove in experiments that our algorithm is fast,
robust and effective.
