TIGNN-RL: Enabling time-sensitive and context-aware intelligent decision-making with dynamic graphs in recommender systems and biomechanics knowledge

Intelligent decision-making in dynamic recommender systems is crucial for capturing temporal user preferences and optimizing long-term user satisfaction. Traditional recommender systems often rely on static modeling, neglecting the temporal dynamics of user-item interactions. To address this limitation, we propose a novel framework, Temporal Interpretability Graph Neural Network with Reinforcement Learning (TIGNN-RL), which integrates dynamic graph neural networks (DGNNs) and Proximal Policy Optimization (PPO) to optimize personalized recommendations. Specifically, our method models user-item interactions as dynamic graphs and utilizes temporal interpretability modules to encode both temporal features and node-specific static features. The temporal interpretability module assigns time-aware and interactions weights to user-item, enabling more time-sensitive and explainable dynamic embeddings. This TIGNN dynamic graph sequential embedding is processed by some LSTM modules to be used as the state of the deep reinforcement learning agent and states. We take a joint approach to training, earn graph embeddings that enable better PPO policy. To evaluate the proposed framework, we conduct experiments on three benchmark datasets: Last.fm 1K, MovieLens 1M, and Amazon Product Review. Results show that TIGNN-RL outperforms state-of-the-art baselines, which use GNNs for augmenting DRL-based RS, in terms of accuracy (NDCG@K) and diversity (ILD@K@K), demonstrating its effectiveness in dynamic and interpretable recommendation scenarios. In this research, some biomechanics knowledge is integrated to further enhance the understanding and application of the proposed framework in scenarios where user behavior is influenced by physical factors.