A collaborative system for recommending a service within the cloud using deep learning

The purpose of this article is to conceive an advanced decision support environment that assists a company in selecting the best service requiring several constraints (expert or machine). The proposed contribution integrates the recommendation in deep learning technique deployed in Cloud. The first step involves preprocessing data using deep learning techniques, particularly NMT and BERT encoders, while the second step uses the neural network to generate recommendations. The neural network's architecture includes two hidden layers consisting of 16 and 8 neurons, configured with the "ReLU" activation function, while the output layer uses the "Softmax" function. The experiments have been conducted on a dataset of 20, 000 services. Results demonstrate that migrating the DL-based approach to cloud computing significantly reduces response time and memory consumption by approximately 15% and 10% for classification tasks, and 50% and 30% for recommendation tasks, respectively. Compared to an earlier version of the proposed approach based on machine learning, the DL-approach improves recommendation accuracy by approximately 3%. However, the results in terms of response time and memory usage remain variable, suggesting that deep learning requires considerable computing resources. The proposed method was benchmarked using evaluation metrics such as accuracy, MAE, response time, and user satisfaction, demonstrating its practical superiority and applicability across various industries.