Deep Neural Network Content Generation for Product Descriptions
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Solution Overview
Problem
The increasing volume of digital content requires efficient methods to quickly generate diverse and accurate content that meets user needs, as manual content creation is time-consuming and inefficient.
Innovation Solution
A content generation method using a deep neural network model trained on historical product description and content information, which selects and generates content phrases to create targeted product descriptions, leveraging techniques like Sequence to Sequence (seq2seq) models and Attention mechanisms to optimize content matching and user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual content creation is used, then content quality can be maintained, but content generation speed and productivity deteriorate
Solution Approach 1:
The patent replaces the manual mechanical content creation process with an automated deep neural network system. The DNN model automatically generates product description content by processing input features such as product titles, images, and specifications, eliminating the need for manual writing while maintaining content quality and significantly improving generation speed.
Solution Approach 2:
The system enables self-service content generation where the DNN model autonomously creates product descriptions without human intervention. The model learns from historical product data and automatically generates optimized content based on input product information, making the content creation process self-sufficient and highly efficient.
2Productivity
If diverse creative content is generated quickly, then user needs are met, but content accuracy and quality may deteriorate
Solution Approach 1:
The patent incorporates feedback mechanisms where the DNN model is trained on historical product data and continuously optimized based on performance metrics. The system learns from past content generation results and user interactions, adjusting its parameters to improve both speed and accuracy simultaneously through iterative refinement.
Solution Approach 2:
The system performs preliminary actions by pre-processing input product information and pre-training the DNN model on extensive historical data before actual content generation. This preparation ensures that when content is generated quickly, it maintains high accuracy because the model has already learned optimal patterns from pre-processed data.
3Measurement precision
If deep neural network model is used for content generation, then content matching accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the content generation system into distinct functional modules: input feature extraction, DNN model processing, and output content generation. This modular architecture allows the complex DNN model to be integrated systematically, managing complexity through clear separation of concerns while maintaining high matching accuracy.
4Adaptability or versatility
If historical data training is performed, then content relevance to user needs improves, but data processing time and resources increase
Solution Approach 1:
The patent performs preliminary action by pre-training the DNN model on historical product data before deployment. This offline training phase processes large amounts of historical data to learn patterns and relationships, so that when the model is deployed, it can quickly generate relevant content without processing time being lost during actual content generation.
Data Source
AI summary
Embodiments of the present application disclose a content generation method and apparatus. The method includes: acquiring product description information; selecting, by using a deep neural network model component, a content phrase matched with the product description information, wherein the deep neural network model component is obtained by training according to a plurality of pieces of historical product description information and historical content of the historical product description information; and generating content corresponding to the product description information based on the selected content phrase.