Cross-platform Dynamic Goods Recommendation System Based on Reinforcement Learning and Social Networks

Aiming at the problems of cold start, gray sheep and sparsity of the traditional collaborative filtering recommendation system, this paper proposes a cross-platform dynamic goods recommendation system based on reinforcement learning and edge computing. First of all, this system models the current friendship relationship networks and potential friendship relationship networks, it also constructs two layers preference prediction models. Then, we consider the frequent change characteristic of social networks and shopping platforms, we design a dynamic reinforcement learning method and edge computing to learn the minimized entropy loss error. Finally, we finish the validation experiments based on the real datasets, the results show the proposed system realizes better link prediction accuracy, and using our proposed system can obtain an obvious increase in the accuracy compared to the existing of collaborative filtering recommendation systems.