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Recommendation systems are highly interested in technology companiesnowadays. The businesses are constantly growing users and products, causing thenumber of users and items to continuously increase over time, to very largenumbers. Traditional recommendation algorithms with complexity dependent on thenumber of users and items make them difficult to adapt to the industrialenvironment. In this paper, we introduce a new method applying graph neuralnetworks with a contrastive learning framework in extracting user preferences.We incorporate a soft clustering architecture that significantly reduces thecomputational cost of the inference process. Experiments show that the model isable to learn user preferences with low computational cost in both training andprediction phases. At the same time, the model gives a very good accuracy. Wecall this architecture EfficientRec with the implication of model compactnessand the ability to scale to unlimited users and products.

EfficientRec an unlimited user-item scale recommendation system based on clustering and users interaction embedding profile