AI Conversion Prediction for User Targeting Accuracy
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Solution Overview
Problem
Current methods for selecting target users for content push, such as advertisements or coupons, are inaccurate due to manual screening, leading to incorrect identification of audience interests and preferences.
Innovation Solution
An object processing method that acquires historical interaction and status features of users and resource objects to predict conversion possibility degrees, determining whether to communicate content based on these predictions using artificial intelligence models like feature generation and conversion prediction models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual screening is used to select target users for content push, then the process is simple to implement, but the accuracy of user targeting deteriorates significantly
Solution Approach 1:
The patent replaces the manual mechanical screening process with an automated AI-based prediction system. The system uses machine learning models to automatically analyze user features and predict conversion probabilities, eliminating the need for manual user selection while significantly improving targeting accuracy.
Solution Approach 2:
The patent introduces an intermediary prediction system that acts as a mediator between the content push system and user data. This intermediary layer processes user features through trained models to generate conversion probability predictions, enabling automated decision-making without direct manual intervention.
2Measurement precision
If AI-based prediction models are used to select target users, then the accuracy of user targeting is improved, but the system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training prediction models offline using historical data before deployment. The models are prepared in advance with all necessary feature processing and parameter tuning completed beforehand, so that during actual operation, only simple feature input and prediction output are required, reducing online system complexity.
Solution Approach 2:
The patent segments the complex prediction system into distinct modular components: feature processing modules that extract user attributes, model prediction modules that generate conversions, and separate training modules that prepare models offline. This segmentation allows each component to be developed and maintained independently, managing overall system complexity.
Data Source
AI summary
An object processing method includes acquiring a historical interaction feature of a user with a historical resource object corresponding to a target resource object, and acquiring a historical status feature of the historical resource object, the historical status feature indicating a change of a resource attribute of the historical resource object. The method further includes determining a conversion prediction feature of the user for the target resource object at a current time based on the historical interaction feature and the historical status feature, and predicting a conversion possibility degree of the user for the target resource object at the current time based on the conversion prediction feature, to determine whether to communicate with the user regarding the target resource object based on the conversion possibility degree.