Multimedia Search Blending Semantic and Latent Feature Vectors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing digital media search engines struggle to provide highly relevant search results due to their reliance on basic keywords and user interaction data, lacking effective integration of rich metadata and latent feature information.
Innovation Solution
The system employs a blended analysis of semantic and latent features to determine similarity relationships between multimedia digital assets and a target asset or search query, using normalized feature vectors and collaborative filtering algorithms to generate ranked search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If basic keyword matching is used for search, then the search system is simple and fast, but the search relevance and accuracy deteriorate
Solution Approach 1:
The patent combines multiple feature types (semantic features from metadata and latent features from user interactions) into a unified similarity scoring system. This merging allows the system to leverage both the interpretability of keyword matching and the contextual understanding of user behavior, improving search relevance without requiring complete redesign of the search infrastructure.
Solution Approach 2:
The system creates a multi-functional search approach that serves multiple purposes: it maintains simple keyword matching for basic queries while simultaneously providing sophisticated similarity-based recommendations for complex search scenarios. This universal approach allows the same system to handle both simple and complex search needs effectively.
2Adaptability or versatility
If only metadata-based semantic search is used, then the search is interpretable and fast, but the ability to capture user preferences and contextual relationships deteriorates
Solution Approach 1:
The system pre-computes latent feature vectors from user interaction data and stores them alongside metadata. This preliminary action allows the system to have user preference information ready when needed, avoiding the need for real-time computation of user behavior patterns during search operations, thus reducing computational overhead while maintaining adaptability to user preferences.
Solution Approach 2:
The patent introduces latent feature vectors as an intermediary representation that bridges metadata and user interactions. These vectors serve as a mediator that captures contextual relationships and user preferences in a compressed form, enabling the system to adapt to user preferences without directly processing raw interaction data during search, thereby reducing computational time.
3Measurement precision
If collaborative filtering is used alone, then user preference accuracy is improved, but the system becomes biased toward popular content and lacks interpretability
Solution Approach 1:
The system creates a composite similarity score that combines metadata-based semantic features and latent feature-based collaborative filtering results. This composite approach integrates the interpretability of semantic analysis with the accuracy of collaborative filtering, producing similarity measurements that are both precise and explainable, avoiding the pitfalls of using either method alone.
4Productivity
If the system processes all available content to provide comprehensive search results, then content coverage is maximized, but the ability to provide targeted and relevant results deteriorates
Solution Approach 1:
The patent applies local quality by weighting and prioritizing different features based on their relevance to each specific search query. Rather than treating all content uniformly, the system adapts the importance of semantic features versus latent features depending on the query context, enabling efficient filtering and ranking that highlights the most relevant content while maintaining comprehensive coverage when necessary.
Data Source
AI summary
Methods and system for determining a similarity relationship between a plurality of digital assets and a target digital asset comprises creating a normalized semantic feature vector associated with a search query, discovering the target asset based on the normalized semantic feature vector, generating a normalized latent feature vector associated with the target asset, comparing the normalized semantic feature vector with semantic feature vectors for each of the digital assets to generate a semantic comparison value, comparing the normalized target latent feature vector with latent feature vectors for each of the digital assets to generate a latent comparison value, blending the semantic comparison vector value with the latent feature comparison vector value to create a target comparison value for each of the digital assets, and reporting the digital assets having the highest target comparison values to the user or group of users.