Two-tower neural network for personalizing service recommendation in cloud environment

Abstract

Effective cloud service recommendation necessitates a deep understanding of both user preferences and the diversity of cloud services. This paper proposes a novel two-tower neural network architecture to address this challenge. By leveraging the power of neural networks, we create concise representations of users and cloud services, enabling highly personalized recommendations. Our two-tower architecture is designed to efficiently scale to the massive scale of cloud environments. Each tower, dedicated to users and cloud services respectively, integrates relevant features and interaction data to generate dense embeddings. Cloud services are then ranked based on their similarity to the user’s embedding, ensuring accurate and tailored suggestions. The proposed two-tower model was evaluated on the WSdream dataset for both regression and classification tasks. Experimental results consistently demonstrated superior performance compared to state-of-the-art recommendation techniques, including k-nearest neighbors and matrix factorization.