Payment Collection Control System Using Behavioral Data for Friend List Generation
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Solution Overview
Problem
Existing Internet-based finance platforms face challenges in efficiently controlling the payment collection process, particularly in automatically determining the optimal group of friends to share expenses equally, which is cumbersome and time-consuming for users.
Innovation Solution
A payment collection control method and device that utilize cumulative behavioral data to generate a one-click equal-share friends list, allowing users to automatically select friends for equal-share collections by analyzing historical data, and adjust the list if necessary to match the current collection order, thereby optimizing the friends list based on participation levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually select friends for equal-share payment collection, then the selection accuracy can be controlled, but the collection efficiency and time consumption deteriorate
Solution Approach 1:
The system pre-generates a one-click friends list by analyzing historical equal-share collection data and user behavioral patterns before the actual collection operation. This preliminary action stores frequently collected friends and their participation patterns, so when a new collection is initiated, the system can immediately present the pre-computed list without requiring users to manually search or select each friend, thereby dramatically improving collection efficiency while maintaining accurate friend selection
Solution Approach 2:
The system creates a simplified copy of the friends list based on historical collection patterns, rather than requiring users to work with the complete contact list or manually reconstruct the selection each time. This copied, optimized list represents the essential information (frequently collected friends) in a condensed format that can be quickly reviewed and confirmed with one click, reducing the operational burden while preserving selection accuracy
2Adaptability or versatility
If a comprehensive friends list is provided for selection, then the adaptability to different collection scenarios is improved, but the device complexity and user burden increase
Solution Approach 1:
Instead of providing a single uniform friends list, the system creates a localized, context-specific one-click friends list tailored to each equal-share collection scenario. By analyzing the specific collection amount, number of participants needed, and historical patterns, the system customizes the friend list to match the current situation, providing high adaptability without overwhelming users with irrelevant options or complex management interfaces
Solution Approach 2:
The system performs preliminary analysis of historical collection data to pre-identify suitable friends for future collections. This advance preparation creates an optimized, scenario-specific friends list that adapts to different collection needs (different amounts, different numbers of participants) without requiring users to manually configure or manage complex list settings each time, thereby achieving versatility with simple user interaction
Data Source
AI summary
A method performed by one or more computers includes obtaining a collection order initiated by a user; determining a quantity of payers for the collection order; determining whether the user selects to invoke a one-click friends list, and if so, generating, a one-click friends list corresponding to the quantity of payers, wherein the one-click friends list is determined by collecting and analyzing cumulative behavioral data of the user based on historical collection orders of the user within a predetermined time period; and receiving user input from the user to determine that the one-click friends list matches an actual friends list for the collection order, and in response, initiating collection corresponding to the collection order.