A deep learning framework enhances personalized advertising by combining reinforcement learning, sentiment analysis, and user behavior modeling. Achieving up to 69% accuracy improvement, the system addresses data sparsity and dynamic preferences, offering scalable solutions for intelligent recommendation in complex, real-time e-commerce environments.
-- Personalized advertising remains one of the most data-intensive and dynamic challenges in modern e-commerce systems. A newly proposed AI framework tackles information overload in e-commerce platforms by applying deep reinforcement learning to enhance personalized recommendation systems. The study reports up to 69% improvement in recommendation accuracy by integrating user behavior analysis, sentiment mining, and optimization algorithms.
The exponential growth of e-commerce has created an environment where users struggle to process massive amounts of product information effectively. Traditional recommendation systems struggle with concept drift, data sparsity, and cold start problems that limit their effectiveness. A recent study introduces an innovative framework combining deep reinforcement learning with advanced natural language processing to transform personalized advertising recommendation generation.
The research addresses fundamental challenges by proposing a multi-layered approach to user preference acquisition. The UPPA method combines user rating matrices with semantic analysis of review texts to construct comprehensive profiles. Through integration of LDA topic modeling with convolutional neural networks for text processing, the system extracts multi-dimensional user interest representations. Experimental validation demonstrates peak recall rates of 0.164 at recommendation list length 30, significantly outperforming baseline methods including BPR, CF, and CFN across multiple scales.
Building upon preference modeling, the work introduces cross-granularity sentiment analysis capturing both fine-grained emotional attitudes toward specific product attributes and coarse-grained overall evaluations. The DeepCGSR model integrates this sentiment matrix with rating data through deep learning fusion, achieving accuracy improvements ranging from 20% to 69% compared to traditional models. At K=5, DeepCGSR reaches 0.359 precision compared to 0.217 for the LFM baseline, demonstrating substantial gains through sentiment-aware modeling.
The research culminates in the DAC-T framework, incorporating Actor-Critic architecture with Transformer mechanisms to optimize recommendation list generation for large-scale dynamic environments. Through list-based recommendations modeled as sequential decision processes, DAC-T achieves NDCG values of 0.432 at 20 epochs and maintains superior performance across precision, recall, F1 score, and MAP metrics while enhancing product diversity through intelligent exploration strategies.
Contributing to this research is Jingtian Zhang, who holds a Master's degree in Computer Science from Georgia Institute of Technology and a Bachelor's degree in Aeronautical and Astronautical Engineering from the University of Washington. Zhang’s professional experience spans distributed backend service development, cloud infrastructure optimization, and large-scale data platform design. Recent work includes developing high-throughput advertising platforms with intelligent caching mechanisms, creating security frameworks for cloud virtualization engines, and leading projects applying large language models to enhance advertising campaign performance through improved bidding precision and intelligent ad generation.
This research makes contributions to both academic understanding and industrial application of personalized recommendation systems. By integrating deep reinforcement learning with sentiment analysis and attention mechanisms, the framework addresses critical challenges in data sparsity, preference dynamics, and recommendation diversity, providing practical guidance for e-commerce platforms seeking to improve user engagement while advancing theoretical foundations for intelligent recommendation system design.
Contact Info:
Name: Jingtian Zhang
Email: Send Email
Organization: Jingtian Zhang
Website: https://scholar.google.com/citations?user=oUCLzdcAAAAJ
Release ID: 89171777
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