Hypothetical Context Inference for Recommendation Queries
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing recommendation systems face challenges in acquiring and utilizing user preference information, especially when preferences change with context, as users find it cumbersome to specify all preferences and may be unaware of subconscious preferences, and current context-aware systems only modify preferences based on immediate context.
Innovation Solution
A system that automatically determines hypothetical contexts by considering current and past contexts, using machine learning to construct a mapping function and estimate distributions over time, location, and weather conditions, allowing for recommendations based on inferred preferences beyond immediate context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users explicitly specify all preference information, then recommendation accuracy is improved, but user operation complexity increases and time consumption increases
Solution Approach 1:
The system automatically acquires and infers user preferences by monitoring user behavior, contextual data, and historical information without requiring explicit user input. The recommendation engine self-adjusts preferences based on observed patterns, eliminating the need for users to manually specify preferences while maintaining high recommendation accuracy
Solution Approach 2:
The patent replaces the mechanical interaction of explicit user preference specification with automated computational inference. Machine learning algorithms and contextual analysis substitute for manual user input, automatically deriving preferences from behavioral data and contextual signals
2Measurement precision
If users explicitly specify all preference information, then recommendation accuracy is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary preference inference continuously in the background by analyzing user behavior patterns and contextual data before recommendations are needed. Historical preferences and contextual models are pre-computed and stored, enabling rapid generation of accurate recommendations without real-time user input
Solution Approach 2:
The system automatically infers and updates user preferences through continuous monitoring of user interactions and contextual signals, eliminating the need for users to invest time in preference specification while maintaining high recommendation accuracy through automated learning
3Adaptability or versatility
If context-aware systems use current context to modify preferences, then adaptability is improved, but the system fails to capture preferences in hypothetical or future contexts
Solution Approach 1:
The system performs preliminary inference of hypothetical contexts and preferences by analyzing patterns in historical data and current contextual signals. It predicts future user preferences and contextual states before they occur, enabling recommendations for scenarios that have not yet materialized but are likely to occur based on observed patterns
Solution Approach 2:
The patent extends the contextual model from only current context to include hypothetical and future contexts as additional dimensions. By incorporating temporal projections and scenario-based reasoning, the system captures preferences across multiple contextual dimensions including past, present, and potential future states
Data Source
AI summary
A system facilitates automatically determining the hypothetical context information or the distribution of hypothetical contexts. During operation, the system receives a request from a user for one or more recommendations. The system also receives a current context substantially associated with the request. The system then determines a hypothetical context for the request, wherein the hypothetical context may be determined by considering several sources of information, including but not limited to the current context, past contexts, and relationships between the current context and past contexts. Next, the system determines one or more recommendations for the user based on the hypothetical context. Finally, the system returns the one or more recommendations to the user.