User Profile Generation via Timestamp-Based Feature Filtering
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Solution Overview
Problem
The existing data processing technologies face inaccuracies in user feature queries due to data storage modes, leading to incorrect user profiles.
Innovation Solution
A data processing method and apparatus that acquire user data and its generation time, generate user features, and store them in a target database only when the generation time is consistent with the last piece of user data, ensuring accurate user profile generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If user features are stored in the data table during the recalculation process, then the data table can be updated sequentially as each piece of user data is processed, but the queried user feature will not be the final user feature calculated based on all user data, resulting in inaccurate query results
Solution Approach 1:
The system performs preliminary actions by storing the generation time of the last piece of user data before the recalculation process begins. During recalculation, this pre-stored generation time is used to compare with the current user data's generation time to determine whether to update the data table, ensuring that only final results are stored without affecting recalculation efficiency.
Solution Approach 2:
The system uses the generation time information that is already present in the user data itself to make the determination of whether to update the data table. The user data's own generation time attribute serves the dual purpose of tracking data versioning and controlling update behavior, eliminating the need for additional external tracking mechanisms.
2Ease of operation
If the server sequentially reads and processes each piece of user data during recalculation, then the recalculation can be completed step by step, but intermediate user features are stored in the data table causing inaccurate query results before recalculation is complete
Solution Approach 1:
The system implements a feedback mechanism by continuously comparing the generation time of current user data with the pre-stored generation time of the last user data. This feedback loop determines whether to proceed with updating the data table, ensuring that updates only occur when appropriate (i.e., when processing the final piece of user data) while maintaining the simplicity of sequential processing.
Data Source
AI summary
Provided are a data processing method and a data processing apparatus based on a user profile, a device, a medium, and a program. The method includes: acquiring multiple pieces of user data and a generation time of a last piece of user data; generating, each time a piece of first user data is read from the pieces of user data, a user feature corresponding to the first user data; storing the user feature corresponding to the first user data into a target database when a generation time of the first user data is consistent with the generation time of the last piece of user data; otherwise, not storing the user feature corresponding to the first user data into the target database; and generating a user profile based on the user feature in the target database.