Offline Experience Ratings for More Accurate Match Recommendations
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Solution Overview
Problem
Current online recommendation systems fail to capture information about offline dating experiences, leading to unsuccessful matches due to factors like poor dating skills, inaccurate profiles, or compatibility issues, which are not reflected in user profile information.
Innovation Solution
A recommendation tool that monitors conversations and surveys users who have met offline to gather feedback on their experiences, using this information to improve future recommendations and provide personalized tips or actions for users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If user profile information is used to form recommendations, then matching can be facilitated among users, but the information does not necessarily provide a good indication of whether the matches will lead to successful offline relationships
Solution Approach 1:
The system collects feedback from users about their offline dating experiences through surveys and conversations. This feedback is then fed back into the recommendation algorithm to improve future recommendations. The feedback loop allows the system to learn from actual outcomes rather than relying solely on static profile information, thereby improving prediction accuracy while maintaining ease of matching.
Solution Approach 2:
The system proactively reaches out to users who have matched and collected feedback before they would naturally disengage. By initiating contact and requesting feedback surveys, the system ensures that data is collected at the optimal time when the user experience is most fresh and memorable, improving the quality and reliability of the feedback data.
2Loss of information
If the system monitors conversations and solicits feedback from users, then information about offline experiences can be captured, but the complexity of the system increases
Solution Approach 1:
The system uses an intermediary feedback collection mechanism that acts as a bridge between user conversations and the recommendation algorithm. Rather than directly analyzing all conversation data, the system uses structured surveys and predefined feedback categories as intermediaries to capture essential information in a manageable format, reducing system complexity while still capturing valuable offline experience data.
Solution Approach 2:
The feedback collection process is segmented into distinct phases: initial matching based on profiles, post-match feedback collection through surveys, and iterative algorithm improvement. This segmentation allows the system to handle information capture and processing in manageable stages rather than attempting to process all data simultaneously, reducing overall system complexity.
3Loss of information
If feedback surveys are transmitted to users after matching, then information about offline experiences can be gathered, but user burden and system resource usage increase
Solution Approach 1:
The system implements partial feedback collection by sending surveys to only those users who have completed offline meetings, rather than all matched pairs. Additionally, the feedback surveys are designed to be concise and focused on key aspects of the dating experience, collecting sufficient information to improve recommendations without imposing excessive burden on users or consuming excessive system resources.
Data Source
AI summary
An apparatus includes an interface and a processor. The processor uses the interface to receive a first set of text from a first user and to transmit the first set of text to a second user. The processor determines, based at least in part on the first set of text, that the first user and the second user had an in-person meeting. The processor uses the interface to transmit a request to the first user for a first set of data. The first set of data includes information about the in-person meeting between the first user and the second user. The processor uses the interface to receive the first set of data from the first user. The processor determines, based on the first set of data, a score of the second user. The processor uses the interface to transmit a notification, based on the score, to the second user.