Digital Human Interactive Recommendation Decision-Making Based on Reinforcement Learning

no code implementations • 6 Oct 2022 • Xiong Junwu, Xiaoyun Feng, Yunzhou Shi, James Zhang, Zhongzhou Zhao, Wei Zhou

Our proposed framework learns through real-time interactions between the digital human and customers dynamically through the state-of-art RL algorithms, combined with multimodal embedding and graph embedding, to improve the accuracy of personalization and thus enable the digital human agent to timely catch the attention of the customer.

Decision Making Graph Embedding +2