Acoustic Voice Query Ranking for Age-Appropriate Content
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Solution Overview
Problem
Current NLP and voice recognition systems fail to consider user attributes such as age and content appropriateness when ranking and presenting search results, leading to irrelevant content recommendations.
Innovation Solution
Leverage acoustic features to identify users as children or adults and adjust relevance scores of content items based on their entity types to ensure appropriate content presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If acoustic features are not considered in content ranking, then the system maintains simplicity in processing, but the content recommendations become less relevant to user demographics
Solution Approach 1:
The system performs preliminary acoustic feature extraction and user demographic classification before content ranking. By determining whether the user is a child or adult in advance (before the actual content matching), the system can then apply appropriate relevance score adjustments to content items based on the user's demographic category, improving recommendation relevance without adding complex real-time processing during the ranking phase
2Adaptability or versatility
If the system only uses phrase matching with entity type weights, then the processing is straightforward, but the content recommendations do not account for user attributes like age appropriateness
Solution Approach 1:
The system applies different relevance score adjustments to different content items based on their entity types (child-friendly vs. adult-friendly) and the user's demographic category. Instead of applying a uniform processing approach to all content, the system locally adapts the relevance scoring for each content item according to its specific characteristics and the user's demographics, ensuring appropriate content recommendations while maintaining clear processing rules
3Reliability
If relevance scores are adjusted based on acoustic features and user demographics, then content appropriateness improves, but the ranking algorithm becomes more complex
Solution Approach 1:
The system modifies the relevance score parameter of content items based on detected acoustic features and user demographic classification. By changing the relevance score parameter (increasing it for appropriate content, decreasing it for inappropriate content), the system ensures content appropriateness while using a simple parameter adjustment mechanism rather than a completely complex ranking algorithm
Data Source
AI summary
The methods and systems described herein leveraging acoustic features of a user to generate and present a personalized content to a user. In one example, the method receives a voice query and determines that the query refers to either a first content item or a second content item. The first content item is associated with a first type assigned with a first score and the second content item is associated with a second entity type assigned with a second score. The method also determines whether the query is from the second entity type. The method ranks the first and the second content items based on this determination and generates for presentation of the first and the second content items based on the ranking. The method also changes the first or the second scores based on this determination and selects one of the first or the second content item for presentation.