  Recommendation systems are highly interested in technology companies
nowadays. The businesses are constantly growing users and products, causing the
number of users and items to continuously increase over time, to very large
numbers. Traditional recommendation algorithms with complexity dependent on the
number of users and items make them difficult to adapt to the industrial
environment. In this paper, we introduce a new method applying graph neural
networks with a contrastive learning framework in extracting user preferences.
We incorporate a soft clustering architecture that significantly reduces the
computational cost of the inference process. Experiments show that the model is
able to learn user preferences with low computational cost in both training and
prediction phases. At the same time, the model gives a very good accuracy. We
call this architecture EfficientRec with the implication of model compactness
and the ability to scale to unlimited users and products.
