Deep Learning-Based User Information Behavior Mining and Personalized Recommendation Optimization

Personalized recommendation systems have become crucial for enhancing user experience and driving engagement in various online platforms. However, existing methods face challenges in accurately modeling high-order user-item relationships, dynamically capturing user preferences, and effectively utilizing multi-modal data. These limitations hinder their ability to deliver relevant, diverse, and context-aware recommendations. To address these challenges, we propose the Graph Attention-based Dynamic Recommendation Framework (GADR). GADR incorporates a graph attention mechanism to prioritize high-order relationships dynamically, a dual-channel structure to simultaneously model long-term and short-term user preferences, and a unified pipeline for integrating textual, visual, and behavioral data. By combining these components, GADR ensures adaptability to dynamic user behavior, improves recommendation diversity, and enhances ranking quality.