Object Determination System for Promoter Selection
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Solution Overview
Problem
Providers, such as live streamers and merchants, face significant time and labor costs in selecting suitable objects to promote to users, as current solutions inadequately determine objects that meet their needs.
Innovation Solution
A method and apparatus for object determination that recalls candidate objects based on various recall policies, determines priority levels using feature representations and a trained priority model, and selects a target object for promotion, optimizing the selection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of objects by promoters is used, then the promoter can carefully evaluate and select suitable objects, but significant time and labor costs are incurred
Solution Approach 1:
The patent replaces the manual mechanical selection process with an automated computer-based system that uses machine learning models and algorithms to recall candidate objects and determine priority levels, thereby eliminating the time and labor costs associated with manual selection while maintaining or improving selection accuracy
Solution Approach 2:
The system enables promoters to automatically receive personalized object recommendations without manual intervention, allowing the system to serve itself by automatically processing promoter features, evaluating candidate objects, and generating priority rankings based on pre-trained models
2Adaptability or versatility
If a large number of objects are available for selection, then the promoter has more choices to find suitable objects, but the complexity of the selection process increases
Solution Approach 1:
The patent segments the large set of available objects into a manageable subset of candidate objects through the recall process, which filters and organizes objects based on promoter features and predefined criteria, thereby reducing the complexity of evaluating a large number of objects while preserving selection flexibility
Solution Approach 2:
The system introduces an intermediary computational layer that automatically evaluates and ranks candidate objects based on promoter characteristics and object features, serving as a mediator between the promoter's needs and the large pool of available objects, thus simplifying the selection process without limiting choices
3Productivity
If automated object recommendation systems are implemented, then time and labor costs are reduced, but the precision of object selection may deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on historical data and pre-recalling candidate objects based on promoter features before the actual selection is needed, which enables fast automated processing while maintaining high accuracy through pre-computed rankings and priority levels
Solution Approach 2:
The system incorporates feedback mechanisms where the priority determination model continuously learns from promoter interactions and selection outcomes, adjusting its predictions to improve accuracy over time while maintaining high automated processing efficiency
Data Source
AI summary
According to the embodiments of the present disclosure, a method, an apparatus, a device, a storage medium and a program product for object determination are provided. The method includes: recalling, from a set of objects, a plurality of candidate objects for a target promoter, the promoter being capable of publishing a guidance content for guiding a user to acquire a corresponding object; determining, based on a first feature of the target promoter and second features of the plurality of candidate objects, priority levels of the plurality of candidate objects; and determining, based on the priority levels, a target object for the target promoter from the plurality of candidate objects.