Shared Digital Experience Matching for More Accurate User Recommendations
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Solution Overview
Problem
Existing online matching/recommendation systems rely on detailed personal information, which users may provide inaccurately or not provide at all, leading to poor compatibility results and user disappointment, and face challenges in maintaining user engagement.
Innovation Solution
A digital experience-based recommendation tool that uses non-linear branching media to probe users' personality traits through choices made during a digital event, generating recommendations based on shared experiences rather than personal information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed personal information is collected for matching, then compatibility assessment can be performed, but users may provide false or idealized information leading to inaccurate recommendations
Solution Approach 1:
The patent extracts the core personality traits needed for matching by presenting users with curated choice scenarios rather than requiring comprehensive personal information. The system extracts only the essential decision-making patterns that reveal personality, eliminating the need for users to provide extensive biographical data that they might falsify.
Solution Approach 2:
The patent introduces a third-party mediator (the recommendation system) that presents choice scenarios and analyzes responses objectively. This intermediary translates user preferences into personality assessments without requiring users to self-report sensitive personal information, thereby reducing the incentive to provide false data while maintaining assessment accuracy.
2Productivity
If traditional profile-based matching is used, then users can be connected based on shared attributes, but user engagement and active participation are reduced
Solution Approach 1:
The patent enables users to actively participate in their own matching process by making choices that reveal their personality traits. Rather than passively receiving matches based on static profiles, users actively construct their preference profile through engagement with choice scenarios, making the system more engaging while maintaining matching efficiency.
Solution Approach 2:
The patent transforms static profile information into dynamic choice-based assessments. The system adapts to user responses in real-time, presenting branching scenarios that evolve based on previous choices. This dynamic interaction increases engagement while efficiently gathering the data needed for accurate matching.
3Measurement precision
If comprehensive user profiles are required, then matching accuracy can be improved, but users may be reluctant to participate due to privacy concerns
Solution Approach 1:
The patent applies partial action by collecting only the specific information needed for personality assessment through choice scenarios, rather than requiring comprehensive personal profiles. This selective data collection approach maintains matching accuracy by focusing on relevant behavioral indicators while reducing privacy concerns that would deter participation.
Data Source
AI summary
An apparatus includes an interface and a processor. The interface sends and receives data over a network. The processor uses the interface to transmit a first media file to a device of a user. The first media file presents a first choice between at least two options. The processor uses the interface to receive from the user a first selection in response to the first choice. In response to receiving the first selection, the processor transmits a second media file to the device. The second media file presents a second choice between at least two options. The processor uses the interface to receive from the user a second selection in response to the second choice. The processor identifies, based in part on the first selection and the second selection, a second user as potentially compatible with the user and transmits to the user a profile of the second user.