Media Item Placement Resource Prediction Model
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Solution Overview
Problem
Existing technologies face challenges in determining the optimal quantity of requisite resources for placing media items, leading to inefficiencies in media placement and potential losses for object providers.
Innovation Solution
A method and apparatus that extract feature information from target media items, use a prediction model to estimate the quantity of requisite resources for multiple placements, and determine the optimal resource quantity based on placement efficiency measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the quantity of requisite resources for media item placement is determined using existing technologies, then the placement process can be completed, but the placement efficiency is insufficient and object providers may suffer losses
Solution Approach 1:
The system performs preliminary actions by extracting feature information from media items and using a prediction model to estimate the quantity of requisite resources before actual placement occurs. This advance prediction allows object providers to determine optimal bid amounts, preventing resource allocation losses and improving placement efficiency by preparing all necessary calculations beforehand.
2Productivity
If feature information extraction and prediction models are used to determine optimal resource quantity, then placement efficiency improves, but the system complexity increases
Solution Approach 1:
The system introduces an intermediary prediction model that acts as a mediator between media item features and resource quantity determination. This prediction model extracts feature information from media items and provides estimated quantities of requisite resources, simplifying the overall system by decoupling the complex prediction logic from the placement execution while improving efficiency.
Data Source
AI summary
A method, apparatus, device and medium for determining quantity of requisite resources for placing a media item are provided. In the method, feature information of a target media item is extracted from relevant data of the target media item. Predicted values of quantity of requisite resources for a plurality of placements in competitive placement of the target media item are obtained using a prediction model based at least on the feature information, the predicted values of the quantity of requisite resources for the plurality of placements respectively corresponding to a plurality of predetermined probabilities of the target media item being placed. Quantity of requisite resources for placing the target media item is determined from the predicted values of the quantity of requisite resources for the plurality of placements, based on a plurality of placement efficiency measures associated with the predicted values of the quantity of requisite resources.