Semantic Graph Weight Adjustment for Contextual Query Responses
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Solution Overview
Problem
Current computing systems lack effective methods to provide personalized and context-based customization using semantic graph data, failing to dynamically adjust weights for connections between objects based on user-specific usage patterns and contexts, which limits the accuracy of recommendations and search results.
Innovation Solution
A computing system that stores semantic graph data and usage information at a fine-grained level, dynamically adjusts weights for connections based on similarity scores between current and prior usage contexts, allowing for customized responses to user requests, such as queries or recommendations, by generating and aggregating usage data across different user groups and contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dynamic weight adjustment based on usage data is implemented, then personalization accuracy is improved, but system complexity increases
Solution Approach 1:
The system pre-computes and stores usage data in a structured format with contextual metadata before queries are executed. This preliminary organization of data allows rapid weight adjustment during query processing without requiring complex real-time computations, thus improving personalization accuracy while managing system complexity.
Solution Approach 2:
The patent introduces an intermediary layer between the semantic graph and usage data that handles weight calculation and adjustment. This intermediary component abstracts the complexity of dynamic weight computation, allowing the core semantic graph to remain relatively simple while still achieving high personalization accuracy through the mediation of usage-based weight adjustments.
2Adaptability or versatility
If fine-grained usage data is collected and stored, then context-based customization is improved, but data storage requirements increase
Solution Approach 1:
The system extracts only the essential contextual features from usage data that are relevant for personalization, rather than storing complete detailed logs of all user interactions. By taking out and storing only the critical contextual attributes needed for weight adjustment, the system achieves effective context-based customization while minimizing data storage requirements.
Solution Approach 2:
Instead of storing all raw usage data and filtering it during processing, the system inverts the approach by pre-processing usage data and storing only the aggregated contextual metrics that are needed for personalization. This inversion reduces storage requirements while maintaining the ability to perform fine-grained context-based customization.
3Measurement precision
If dynamic weight adjustment is performed for each query, then recommendation relevance is improved, but processing time increases
Solution Approach 1:
The system performs preliminary computations to pre-calculate baseline weights and organize usage data in an optimized structure before queries are received. This preliminary preparation enables rapid weight adjustment during actual query processing, improving recommendation relevance without incurring excessive processing time delays.
Solution Approach 2:
The system applies partial weight adjustments based on the most relevant contextual factors rather than recomputing all weights from scratch for each query. By performing partial updates using only the necessary contextual information, the system maintains high recommendation relevance while significantly reducing processing time compared to full re-computation.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for context-based customization using semantic graph data. In some implementations, semantic graph data indicating objects and relationships among the objects is stored. Usage data is generated to indicate (i) levels of usage of the objects and (ii) contexts in which the objects are used. A query is received that is associated with a user and that includes data indicating a context of the user. Customized weights for connections among the objects is determined based on (i) the levels of usage indicated by the usage data and (ii) scores indicating a degree of similarity among the contexts indicated by the usage data and the context of the user. A response to the query is provided based on the customized weights for the connections among the objects indicated by the semantic graph data.