Personalized Recommendation Rewards Based on User Action Types
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Solution Overview
Problem
Existing recommendation systems categorize users based on account types, leading to generic and less effective recommendations, failing to leverage the exponential growth and accessibility of user data for personalized incentives.
Innovation Solution
A system that selects action types based on underlying account requirements, proactively generating recommendations and structuring them as rewards for completing actions, thereby incentivizing user behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If users are categorized based on account types, then recommendations can be generated systematically, but the recommendations become generic and less effective
Solution Approach 1:
The patent segments the recommendation generation process into multiple dimensions: account type classification, action type identification, and specific user behavior tracking. This multi-level segmentation allows the system to maintain systematic processing while achieving granular personalization, resolving the contradiction between ease of generation and personalization accuracy.
Solution Approach 2:
The patent applies local quality by customizing recommendations based on specific action types and user behaviors rather than applying uniform treatment to all users of an account type. Each user receives tailored recommendations based on their specific actions (e.g., login frequency, feature usage), enabling precise personalization while maintaining systematic processing through the structured action-type framework.
2Productivity
If a proactive approach is used to select action types, then user engagement increases, but system complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-defining action types and their associated recommendations before users perform actions. The system proactively selects appropriate action types based on account requirements and prepares corresponding recommendations in advance, which increases user engagement while managing system complexity through structured pre-processing rather than complex real-time analysis.
Solution Approach 2:
The patent uses parameter changes by dynamically adjusting recommendation parameters based on selected action types and user responses. The system modifies recommendation content, timing, and delivery based on changing user states and actions, enabling high engagement through adaptive personalization while maintaining manageable complexity through parameter-based control rather than structural complexity.
3Device complexity
If generic recommendations are provided to account types, then implementation is simple, but user participation decreases
Solution Approach 1:
The patent applies dynamics by making recommendations adaptive and responsive to user actions rather than static and uniform. The system dynamically selects and adjusts recommendations based on real-time user behavior data, ensuring high participation through relevant, timely content while maintaining implementation simplicity through automated action-type-based selection rather than complex manual customization.
Data Source
AI summary
In an example embodiment, a system is described for customizing user accounts based on user actions, which may include receiving, via a user device, a first user input requesting to create an account for a user. The system may further receive, via the user device, a second user input selecting an account type for the account from a plurality of account types. In response to these user inputs, the system may retrieve, from a server, one or more account requirements for the account type, determine that the user meets the one or more account requirements and create an account in response to determining that the user meets the one or more account requirements.