  Recommender systems is one of the most successful AI technologies applied in
the internet cooperations. Popular internet products such as TikTok, Amazon,
and YouTube have all integrated recommender systems as their core product
feature. Although recommender systems have received great success, it is well
known for highly skewed datasets, engineers and researchers need to adjust
their methods to tackle the specific problem to yield good results. Inability
to deal with highly skewed dataset usually generates hard computational
problems for big data clusters and unsatisfactory results for customers. In
this paper, we propose a new algorithm solving the problem in the framework of
matrix factorization. We model the data skewness factors in the theoretic
modeling of the approach with easy to interpret and easy to implement formulas.
We prove in experiments our method generates comparably favorite results with
popular recommender system algorithms such as Learning to Rank , Alternating
Least Squares and Deep Matrix Factorization.
