Context-Aware Semantic Projection for Dynamic User Interest Measurement
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Solution Overview
Problem
Conventional recommendation systems fail to capture spontaneous user interests and contextual factors, leading to irrelevant recommendations, as they rely on static user profiles and absolute similarity measures that do not account for situational context.
Innovation Solution
A system that determines an interest measure for a user and a resource in a given context using a semantic projection model, which creates weighted concepts for users, resources, and contexts, allowing for the calculation of similarities and providing dynamic interest measurements based on user actions and context-specific ratings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recommendation systems use static user profiles and absolute similarity measures, then the system complexity is reduced, but the accuracy of recommendations deteriorates because spontaneous user interests and contextual factors are not captured
Solution Approach 1:
The patent transforms static user profiles into dynamic situational profiles that adapt to current context. The system continuously updates user interest representations based on real-time contextual factors such as location, time, and current activity, allowing the recommendation system to capture spontaneous user interests while maintaining manageable complexity through structured contextual modeling
Solution Approach 2:
The patent introduces contextual dimensions to the traditional user-resource matching framework. By adding situational context as an additional dimension (beyond just user profile and resource characteristics), the system achieves more accurate recommendations without proportionally increasing complexity, as the contextual layer provides a structured way to incorporate multiple factors
2Productivity
If recommendation systems use absolute similarity measures between users or resources, then the computational efficiency is improved, but the relevance of recommendations deteriorates because contextual factors are ignored
Solution Approach 1:
The patent segments the similarity calculation into multiple components: base user similarity, resource similarity, and contextual similarity. Each component can be computed independently and then combined, maintaining computational efficiency while improving relevance. The segmentation allows the system to selectively compute only necessary components based on available data
Solution Approach 2:
The patent changes the parameters used in similarity calculations from fixed absolute measures to dynamic contextual measures. The system adjusts similarity weights and parameters based on situational context, allowing more relevant comparisons in different contexts while maintaining computational tractability through predefined parameter adjustment rules
3Reliability
If user profiles are updated frequently to capture spontaneous interests, then the responsiveness to user needs is improved, but the data processing load increases
Solution Approach 1:
The patent implements periodic updates to user profiles based on contextual triggers rather than continuous updates. The system monitors contextual factors and updates profiles periodically when significant contextual changes occur (such as location changes or time-based events), maintaining responsiveness to user needs while reducing unnecessary processing during stable periods
Solution Approach 2:
The system employs self-service mechanisms where contextual data is automatically collected and processed without requiring intensive manual intervention. Automated contextual monitoring and profile updating reduce the data processing load by leveraging existing system infrastructure and algorithms to handle updates efficiently based on predefined rules
Data Source
AI summary
A computer-implemented system may include determination of a similarity of a semantic projection of a set of interests of a first user to a semantic projection of a first resource, wherein the semantic projection of the set of interests of the first user and the semantic projection of the first resource conforms to a common semantic projection model. Also included may be determination of a semantic interest of the first user for the first resource in a first context based on the similarity of the semantic projection of the set of the interests of the first user to the semantic projection of the first resource, and determination of an interest measure associated with the first user, the first resource and the first context based on a rating prediction for the first user to the first resource in the first context and on the determined semantic interest of the first user for the first resource in the first context.