User Action Prediction Model for Real-Time Service Conversion
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Solution Overview
Problem
Existing user action prediction systems face challenges in achieving high prediction accuracy and real-time performance due to the variety of user actions, leading to insufficient conversion probability predictions and delayed benefit conferment.
Innovation Solution
A prediction system that utilizes a learning model to analyze the action history of users and predict usage results, incorporating a processor to acquire and process user action data, and execute corresponding processing in real-time based on predicted outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a prediction system is built to analyze user actions, then prediction accuracy can be improved, but real-time performance deteriorates due to the complexity of processing various user actions
Solution Approach 1:
The prediction system is segmented into multiple independent prediction models, each specialized for specific user actions (e.g., purchase prediction model, bookmark prediction model). This allows parallel processing of different action types, improving real-time performance while maintaining comprehensive prediction accuracy through the collective capability of multiple specialized models.
2Measurement precision
If the system waits for users to perform predetermined operations before conferring benefits, then prediction accuracy improves, but real-time performance deteriorates as benefits are delayed
Solution Approach 1:
The system performs preliminary prediction of user actions before the users actually complete them. By analyzing current user behavior patterns and predicting future actions in advance, the system can confer benefits proactively based on predicted usage results, eliminating the delay of waiting for predetermined operations to occur.
Solution Approach 2:
The system implements a feedback mechanism where predicted usage results trigger immediate benefit conferral, and the outcomes of these benefit conferrals are fed back into the prediction models. This continuous feedback loop allows the system to learn from actual user responses and improve prediction accuracy over time while maintaining real-time benefit delivery.
Data Source
AI summary
Provided is a prediction system including: a learning model in which a relationship between an action history of each of a plurality of users who used a service in the past and a usage result of the service included in the action history of each of the plurality of users is learned; and at least one processor, the at least one processor being configured to: acquire the action history of a user using the service; predict, based on the action history of the user using the service and the learning model, the usage result of the user using the service; and execute processing corresponding to the usage result predicted.