Deep Reinforcement Learning Recommendation System Algorithm Based on Multi-Level Attention Mechanisms

• Published in: Electronics (Basel) Vol. 13; no. 23; p. 4625
• Main Authors: Wang, Gaopeng, Ding, Jingyi, Hu, Fanlin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2024
• Subjects: Accuracy; Algorithms; Analysis; Collaboration; Datasets; Deep learning; Depth profiling; Effectiveness; Information overload; Machine learning; Markov analysis; Multilayers; Neural networks; Real time; Recommender systems; Social networks; User behavior; User feedback; User satisfaction
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics13234625

Traditional recommendation systems, which rely on static user profiles and historical interaction data, frequently face difficulties in adapting to the rapid changes in user preferences that are typical of dynamic environments. In contrast, recommendation algorithms based on deep reinforcement learning are capable of dynamically adjusting their strategies to accommodate real-time fluctuations in user preferences. However, current deep reinforcement learning recommendation algorithms encounter several challenges, including the oversight of item features associated with high long-term rewards that reflect users’ enduring interests, as well as a lack of significant relevance between user attributes and item characteristics. This leads to an inadequate extraction of personalized information. To address these issues, this study presents a novel recommendation system known as the Multi-Level Hierarchical Attention Mechanism Deep Reinforcement Recommendation (MHDRR), which is fundamentally grounded in a multi-layer attention mechanism. This mechanism consists of a local attention layer, a global attention layer, and a Transformer layer, allowing for a detailed analysis of individual attributes and interactions within short-term preferred items, while also exploring users’ long-term interests. This methodology promotes a comprehensive understanding of users’ immediate and enduring preferences, thereby improving the overall effectiveness of the system over time. Experimental results obtained from three publicly available datasets validate the effectiveness of the proposed model.