Design and algorithm optimization of personalized consumer notification system based on artificial intelligence

In the era of information overload, personalized consumer notification system has become an important tool to meet consumers' demand for accurate information. This paper presents a personalized consumer notification system design and algorithm optimization scheme based on artificial intelligence (AI). The system adopts hierarchical architecture, including data collection, user portrait construction, recommendation algorithm application and result displ510900ay and interaction, and realizes efficient information push through deep learning and reinforcement learning (RL) technology. In terms of algorithm optimization, addressing the limitations of existing recommendation algorithms, a Hierarchical Hybrid Recommendation Algorithm (HHRA) is proposed. This algorithm integrates multi-source data including user behavior sequences, item content features, social network relationships, and geographical location information, utilizing deep feature extraction, social graph embedding, geospatial encoding, and Q-Learning strategies for optimization. Experimental validation shows that HHRA significantly outperforms traditional algorithms in accuracy (with an increase of 22.1% in F1 score), diversity (a 20.6% improvement in ILAD@10), and cold-start performance (a 104% increase in new user CTR). User satisfaction surveys indicate that the proportion of high satisfaction in the HHRA group reaches 65%, markedly higher than the control group.