Event Inception System Matching User Interests
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Solution Overview
Problem
Existing systems fail to capture and aggregate unique user preferences to create new events by matching consumer interests with content providers and venues, lacking the ability to trigger event creation and ownership determination.
Innovation Solution
A system and method that analyze user preferences from multiple groups to identify overlapping and complementary interests, proposing events and prompting ownership through a processor-based system, enabling the creation of new events by aggregating content consumer, provider, and venue parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users only search for and demand existing events without planning, then event discovery is simplified, but event creation and customization are limited
Solution Approach 1:
The system performs preliminary actions by automatically analyzing user preferences, aggregating common interests, and proposing event concepts before users actively search for events. This preliminary event creation process enables users to both discover and create events through a unified interface, resolving the contradiction between simplified discovery and limited customization.
2Measurement precision
If the system aggregates detailed user preferences and parameters, then event matching precision improves, but system complexity increases
Solution Approach 1:
The system implements self-service by automatically collecting user preferences through interactions, aggregating common interests, and generating event proposals without requiring complex manual configuration. The system serves itself by continuously learning from user behavior data, maintaining high matching precision while keeping the interface simple for users.
3Productivity
If the system automatically proposes events based on aggregated interests, then event creation efficiency increases, but ownership determination becomes more complex
Solution Approach 1:
The system uses feedback mechanisms to manage ownership complexity. When events are automatically proposed based on aggregated user interests, the system tracks user responses, acceptance rates, and engagement metrics. This feedback loop allows the system to refine ownership attribution algorithms, maintaining high event creation efficiency while progressively improving ownership determination accuracy through iterative learning.
Data Source
AI summary
Common interests of at least two different groups of users are captured and analyzed to create a proposal for an event that would satisfy the interests of each group of users. One group of users may be consumers of content, another group of users may be providers of content and a further group of users may, for example, be venue providers. Once an event is proposed, ownership of the event may be prompted for.