Voice Request Analysis for Personalized News Recommendation
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Solution Overview
Problem
Traditional news recommendation methods fail to provide personalized news to users based on their specific attributes and preferences, often prioritizing timeliness and content quality over user-specific demands.
Innovation Solution
A method and apparatus that analyze voice requests to determine target attributes from a preset user attribute set, acquiring corresponding user attribute information to select and recommend news from a database based on similarity analysis, improving the relevance of news recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional news recommendation methods are used that prioritize timeliness and content quality, then news selection efficiency is improved, but personalization and relevance to user preferences deteriorate
Solution Approach 1:
The recommendation system segments users into different user groups based on their attributes (such as location, device type, usage habits) and provides different news recommendations for each segment. This segmentation approach enables personalized recommendations while maintaining efficient automated processing, resolving the contradiction between productivity and adaptability.
Solution Approach 2:
The system changes the parameter of news recommendation from a uniform approach to a multi-dimensional approach by incorporating various user attributes (location, device, usage patterns) as parameters. This allows the system to maintain efficient automated operation while adapting to different user preferences through parameter variations.
2Measurement precision
If voice request analysis is performed to determine user attributes, then news recommendation pertinence is improved, but system complexity increases
Solution Approach 1:
The voice analysis system automatically extracts user attributes from voice requests without requiring manual user input or complex configuration. The system self-services by autonomously analyzing voice content, identifying user intent, and determining relevant attributes, thereby improving measurement precision while keeping the operational complexity manageable.
Solution Approach 2:
The system introduces a voice analysis module as an intermediary between the user's voice request and the news recommendation engine. This intermediary automatically processes and structures the unstructured voice data into usable user attribute information, improving identification accuracy while isolating the complexity within a dedicated module.
Data Source
AI summary
Embodiments of the present disclosure disclose a method and apparatus for recommending news. A specific embodiment of the method comprises: receiving a voice request for playing news; analyzing the voice request, and determining a target attribute associated with the voice request from a preset user attribute set based on an analysis result; acquiring user attribute information corresponding to the target attribute; and selecting out target news from a to-be-recommended news database for recommendation based on the analysis result of the voice request and the user attribute information corresponding to the target attribute. Such embodiment realizes news recommendation based on voice interaction and is capable of improving the pertinence of news recommendation.