Combination service recommendation based on deep learning

Aiming at the QoS parameter volatility and uncertainty of service computing environment, high dimensional input of deep neural networks and reinforce learning were considered to resolve dynamic service optimization and recommendation in complex cloud environment, so an intelligence collaborative service recommendation model based on three-layer MAS architecture was built. Specifically, state transfer model of the business process was constructed from the perspective of activity-level, and the local QoS and global service collaboration was evaluated, which could solve the time-dependent problem of state transition in large-scale business process modeling. A Business Process as a service Recommendation algorithm based on time-series Evolution Deep Q-Learning (EDQL-BPR) was proposed, and the Q value update strategy based on particle swarm optimization was designed, which improved the optimization efficiency of learning Agent of deep neural network and the recommendation quality of BPaaS service, and a-chieved a good balance between efficiency and adaptability under dynamic environment. © 2024 CIMS. All rights reserved.