A dynamic recommendation strategy for Chinese language teaching resources driven by reinforcement learning

Abstract

Intelligent recommendations are becoming more and more popular in Chinese language learning (CLL) platforms, yet current approaches lack centralization, slowness in adapting to the learners, and poor interaction modeling. The classical forms of Reinforcement Learning (RL) are typically not effective in providing individualized recommendations of resources in the context of dynamic learning. As a way of resolving these limitations, the focus is on developing a flexible and decentralized recommendation system that is efficient in responding to diverse learner profiles and changing patterns of engagement. A new Decentralized Distributed Proximal using Dueling Deep Q Network (D2P-D2QN) is presented, which combines the accuracy of the D2QN estimation with the robustness of proximal policy optimization in a multi-agent setting that is distributed. A Chinese Learning Resource dataset containing 2000 records of learner performance logs, resource metadata, engagement durations, and feedback indicators supports this system. Preprocessing includes missing-value correction for data integrity, normalization for stable learning, and sequence structuring to model temporal learner behavior. Feature extraction relies on an autoencoder to generate compact latent representations of learner-resource interactions. Within the proposed mechanism, the proximal component manages policy refinement, while the dueling Q network handles advantage estimation and value decomposition. Decentralized distributed coordination enables multiple agents to update decisions concurrently for scalable and responsive recommendation generation. Implementation utilizes a deep learning environment supported by Python and RL libraries. Experimental results demonstrate 95.6% accuracy, 93.2% F1-score, 0.98 AUC, 0.94 MCC, 160 ms runtime, 72 ms latency, learning willingness 4.5, engagement 4.6, and 94.1% interaction success compared to conventional RL-based recommenders. The overall framework indicates strong potential for next-generation adaptive educational systems that require both precision and scalability in resource delivery.