AI-Powered Intelligent Pricing System: A Price Prediction Solution Combining Large Language Models and Deep Neural Networks

This article introduces an innovative open-source project—the AI Product Price Predictor—which combines large language models (LLMs) with deep neural networks to build an intelligent pricing assistant, supporting local Ollama deployment. The project uses LLMs to generate structured product summaries, then applies deep neural networks to predict market prices, providing pricing solutions for scenarios like e-commerce and second-hand transactions.

In e-commerce and second-hand markets, pricing is a complex challenge: too high leads to unsold goods, too low results in profit loss. Traditional methods rely on manual experience or simple rules, making it hard to capture market dynamics and subtle differences in product characteristics. Product pricing is influenced by multiple factors such as brand, condition, supply and demand, seasonality, and competitors, forming a highly non-linear problem that requires ordinary sellers to conduct extensive research and accumulate experience.

The core of the project is the collaborative design of LLMs and deep neural networks:

1. LLM Product Summary Generation: Use LLMs (e.g., Llama 3.2) to understand product descriptions and generate structured summaries (such as product category, brand model, storage capacity, condition, etc.) suitable for machine learning processing, supporting local Ollama deployment.
2. Deep Neural Network Price Prediction: Input structured summaries into deep neural networks to learn complex mappings between features and prices, capturing the premium or negative impact of feature combinations.

• Local Deployment Support: Implement local model operation via Ollama, protecting data privacy, reducing costs, and supporting offline use—ideal for scenarios involving sensitive information or batch processing.
• Modular Design: LLM summary generation and neural network prediction are independent modules, allowing separate optimization and replacement, making it easy to try different models and conduct debugging analysis.

The project has wide applications:

• Second-hand Trading Platforms: Provide pricing suggestions for sellers on platforms like Xianyu and Zhuanzhuan, reducing decision-making burdens.
• E-commerce Merchant Tools: Help small and medium-sized sellers evaluate pricing space for new products or adjust prices dynamically, enabling refined strategies by combining competitor data.
• Price Monitoring and Arbitrage: Analyze product data in batches to identify pricing anomalies, discover arbitrage opportunities, or monitor price trends.

The current project has room for improvement:

• Data Dependency: Prediction accuracy relies on the representativeness of training data, which needs to cover more categories and time periods.
• Market Dynamics: Prices are highly volatile due to supply and demand; models need regular updates, and time-series components can be introduced to learn temporal changes.
• Multimodal Expansion: Currently only processes text; combining product images (using visual models to extract condition information) can improve accuracy.

This project demonstrates the collaborative paradigm of 'large model + small model': LLMs process unstructured inputs to extract information, while dedicated neural networks perform prediction tasks, balancing flexibility and accuracy. This represents the trend of AI applications evolving from single models to multi-model collaboration, providing reference for developers in designing system architectures and balancing performance and cost.