In-Vehicle App Recommendation System Using Consensus Ratings
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Solution Overview
Problem
In-vehicle infotainment systems face challenges in recommending relevant apps to users due to the overwhelming number of available apps, with existing recommendation engines only considering simple parameters like app category, failing to account for user context and usage trends.
Innovation Solution
A method and system that collect and analyze user/application rating data, including implicit ratings from app usage, to calculate inferred ratings and provide personalized app recommendations using both user-driven and application-driven consensus ratings, combined with external inputs, to synthesize recommendations for users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple parameters like app category are used for recommendations, then the recommendation system is easy to implement, but the accuracy and relevance of recommendations deteriorates
Solution Approach 1:
The patent transforms simple categorical parameters into multi-dimensional feature vectors that include usage frequency, recency, duration, and contextual information. This parameter expansion enables the system to capture nuanced user preferences while maintaining computational efficiency through structured data processing.
Solution Approach 2:
The system adds multiple dimensions to the recommendation evaluation by incorporating temporal patterns, usage intensity metrics, and contextual factors alongside traditional category-based filtering. This dimensional expansion allows for more precise user-app matching without proportionally increasing system complexity.
2Measurement precision
If more comprehensive user context and usage trend analysis is performed, then recommendation accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system pre-processes and stores usage data in structured formats that enable rapid retrieval and analysis. By preparing data in advance and maintaining efficient data structures, the system can perform comprehensive analysis without significant real-time computational overhead.
Solution Approach 2:
The patent implements continuous background processing of usage data that operates independently of recommendation requests. This allows the system to maintain up-to-date user profiles and usage trends without blocking the main recommendation flow, effectively separating data processing from service delivery.
3Measurement precision
If implicit ratings based on app usage data are collected, then the system can provide more personalized recommendations, but data privacy concerns and user trust issues arise
Solution Approach 1:
The system derives ratings automatically from observable usage behavior patterns rather than requiring explicit user input or sensitive data collection. This self-service approach uses naturally occurring usage data to infer preferences, eliminating the need for separate data gathering mechanisms that would raise privacy concerns.
Solution Approach 2:
The patent introduces usage pattern analysis as an intermediary layer between raw user behavior data and recommendation outputs. This intermediary process transforms detailed usage information into abstracted preference models, filtering out personally identifiable information while preserving recommendation relevance.
Data Source
AI summary
A method and system for recommending applications to users of in-vehicle infotainment systems are disclosed. Application rating data from many road vehicle infotainment system users are collected on a central server, including both explicit ratings and implicit ratings. Implicit ratings may be calculated based on application usage data. The user/application rating data is filtered for relevance, and then analyzed to determine inferred ratings for user/application relationships where no rating exists. The inferred ratings are calculated using both a user-driven consensus rating calculation and an application-driven consensus rating calculation. The inferred ratings, along with optional cyberspace-based external inputs, are used to synthesize application recommendations for users. The synthesized recommendations for application consideration are provided to the appropriate user via downloading from the central server to the infotainment system in the user's vehicle.