Context-Aware Recommendation Manager Using Multi-Granular Context Modules
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Solution Overview
Problem
Current recommendation systems on mobile devices lack precision due to small screen sizes and the need for more accurate information, as they fail to effectively integrate granular contextual information to provide tailored recommendations.
Innovation Solution
A context-aware recommendation manager that detects user context through a multi-granular context module, pre-filters contextual information, and models context combinations using historical network navigation data to select the best recommendations based on granular structure and user profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recommendation systems integrate multiple contextual information sources to improve recommendation accuracy, then recommendation precision improves, but system complexity increases
Solution Approach 1:
The patent segments the recommendation system into distinct functional modules: a context detector module that identifies relevant contextual information, a context analyzer module that processes the detected context, and a recommendation generator module that produces tailored recommendations. This segmentation allows each module to handle specific tasks independently, improving recommendation precision through comprehensive context integration while managing system complexity through modular architecture.
Solution Approach 2:
The system performs preliminary context detection and analysis before generating recommendations. The context detector module proactively identifies contextual information such as user location, time, device type, and usage patterns in advance, and the context analyzer module processes this information to determine relevant context combinations. This preliminary action enables the recommendation system to prepare personalized recommendations ahead of time, improving precision without adding operational complexity during actual recommendation delivery.
2Reliability
If the system processes and integrates rich contextual information to provide tailored recommendations, then user satisfaction improves, but computational resources increase
Solution Approach 1:
The patent extracts only the most relevant contextual information from the available data sources using the context detector module. Instead of processing all possible contextual data, the system identifies and extracts specific context elements such as user location, time of day, device type, and usage patterns that are most predictive of user preferences. This selective extraction reduces computational resource consumption while maintaining high user satisfaction by focusing on the most impactful contextual factors.
Solution Approach 2:
The system applies partial processing to contextual information by analyzing only the subset of context data that has the greatest impact on recommendation quality. The context analyzer module evaluates multiple context combinations but focuses computational effort on the most promising combinations rather than exhaustively processing all possible combinations. This partial action approach achieves high user satisfaction through targeted analysis while conserving computational resources.
Data Source
AI summary
In accordance with aspects of the disclosure, systems and methods are provided for managing context aware recommendations by providing recommendations to a user in response to a query related to the user by integrating contextual information of a context related to the user in a recommendation model while considering a granular structure of the context and the contextual information thereof.