Content Recommendation Using NLP Domain Knowledge and Tagging
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Solution Overview
Problem
Conventional content-recommendation approaches fail to accurately match content with individual user interests due to their reliance on simple keyword matching, leading to inefficient content delivery in mobile applications, where users often need to browse through numerous pieces to find relevant information.
Innovation Solution
A content-recommendation system that employs natural language processing (NLP) and domain knowledge, including hierarchical domain knowledge and domain-knowledge graphs, to generate feature tags for content pieces and attribute tags for users, enabling precise matching of content to user interests by extracting and ranking keywords based on their relevance within specific domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional keyword matching is used for content recommendation, then the system is simple to implement, but the recommendation accuracy is low
Solution Approach 1:
The patent segments the content recommendation process into multiple independent modules: user profile analysis module, content analysis module, recommendation algorithm module, and feedback module. Each module processes specific aspects independently, allowing the system to achieve high recommendation accuracy through comprehensive analysis while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediary elements such as user profiles (mediating user preferences) and content tags (mediating content characteristics) that bridge the gap between users and content. These intermediaries enable sophisticated matching without requiring direct complex interactions, thus improving accuracy while controlling system complexity.
2Loss of time
If conventional content recommendation is used, then the system operates quickly, but users must browse through numerous pieces to find relevant information
Solution Approach 1:
The patent performs preliminary analysis of user preferences and content characteristics in advance, building user profiles and content tags before the actual recommendation occurs. This preliminary action ensures that when users need content, the system can quickly retrieve and present relevant items without requiring users to browse through numerous pieces, thus reducing time loss while improving overall content delivery efficiency.
Solution Approach 2:
The patent replaces the mechanical browsing process with an intelligent recommendation system that uses algorithms to automatically match users with relevant content. This substitution eliminates the need for users to manually browse through numerous pieces, significantly reducing time loss while improving productivity through automated, accurate content delivery.
3Adaptability or versatility
If personalized content recommendation is implemented, then user satisfaction improves, but the system complexity increases
Solution Approach 1:
The patent implements a universal user profile structure and content tagging system that can accommodate diverse user preferences and content types through a unified framework. This multi-functional approach enables personalized recommendations across different domains and user types without requiring separate complex systems for each case, thus improving adaptability while controlling system complexity.
Solution Approach 2:
The patent achieves personalization by dynamically adjusting recommendation parameters based on user profiles and context, rather than changing the overall system architecture. This allows the system to adapt to individual users through parameter modifications within an existing framework, improving personalization capability while avoiding the complexity of completely custom systems for each user.
Data Source
AI summary
One embodiment provides a method and system for recommending content to users. During operation, the system can select a content piece from a content library and extract, by a computer using a natural language processing (NLP) technique, one or more keywords from the content piece. The system can determine a domain associated with the content piece based on the extracted keywords and obtain domain knowledge of the determined domain. The system can generate a feature tag for the content piece based on the extracted keywords and the obtained domain knowledge, and generate an attribute tag for a user based on historical data associated with the user. The system can then recommend one or more content pieces from the content library to the user based on feature tags associated with the one or more content pieces and the attribute tag for the user.