User Preference Input for Driving Recommendation Matching
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Solution Overview
Problem
Existing information providing methods fail to accurately match user preferences, as they rely on automated selection of similar users without user input or feedback, leading to mismatched recommendations.
Innovation Solution
An information providing method that allows users to select and input preference information directly, enabling the system to associate users based on both evaluation data and user-defined preferences, ensuring more accurate recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated selection of similar users is used without user input, then the system operation is simplified and processing speed is improved, but the accuracy of matching user preferences deteriorates
Solution Approach 1:
The system implements feedback by allowing users to review automatically selected similar users and provide corrections or additional selections. Users can view the selected similar users, delete inappropriate selections, and add their own choices. This feedback loop ensures that the final similar user selection accurately reflects the target user's preferences while maintaining efficient automated processing.
2Ease of operation
If automated selection of similar users is used without user input, then the device complexity is reduced and ease of operation is improved, but the reliability of information provision deteriorates
Solution Approach 1:
The system empowers users to take control of the similar user selection process by allowing them to review automated selections, delete inappropriate users, and add their own choices. This self-service approach ensures that users can customize the selection to match their specific preferences, thereby improving the reliability of information provision while maintaining ease of operation.
3Measurement precision
If users are allowed to select similar users manually, then the accuracy of matching user preferences is improved, but the device complexity and operation difficulty increase
Solution Approach 1:
The system performs preliminary automated selection of similar users based on evaluation data before presenting the selection to the user. This preliminary action reduces the complexity of manual selection by providing a pre-filtered list of candidates, requiring users only to review and make minor adjustments rather than selecting from scratch, thus balancing accuracy with simplicity.
4Reliability
If users are allowed to select similar users manually, then the reliability of information provision is improved, but the ease of operation and processing efficiency deteriorate
Solution Approach 1:
The system merges automated selection capabilities with manual user input by combining the strengths of both approaches. The automated system performs initial selection based on evaluation data to ensure efficiency, while user input adds reliability by allowing corrections and custom selections. This hybrid approach maintains processing efficiency while improving reliability.
Data Source
AI summary
An information providing method and apparatus a) transmits a request via a network, for recommended driving information for a first user to be received and displayed on a display of the first user, b) receives from the network driving data from a plurality of vehicles about how a plurality of users drive their vehicles including the first user, c) extracts a similar user from among the plurality of users who drives a vehicle with a predetermined similarity to how the first user drives a vehicle, and determines recommended driving information of the similar user from the similar user's driving history, d) transmits over the network to the first user the recommended driving information of the similar user, and e) displays on a display of the first user the recommended driving information of the similar user.