User-Specific Incentive Pricing Allocation Models
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Solution Overview
Problem
Conventional data models fail to accurately select items and generate transmissions/interfaces associated with offers for goods, services, and electronic items likely to engage users, resulting in wasted processing, networking, memory, and computing resources.
Innovation Solution
The development of improved data model generation and use methods, including aggregating user profile data sets, determining adjustment unit counts, identifying electronic item representations, and applying user profile data, adjustment unit counts, and electronic item transactability data to allocating data models to determine accurate adjustment unit allocations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data models are used to select items and generate transmissions/interfaces, then processing and networking resources are consumed, but the accuracy of item selection and user engagement is insufficient
Solution Approach 1:
The system performs preliminary actions by pre-processing user profile data and pre-configuring allocating data models before actual item selection and transmission generation. This includes aggregating user profile data sets, determining adjustment unit counts, and preparing electronic item representations in advance, so that when item selection is needed, the system can quickly apply pre-configured models rather than processing everything from scratch, thereby improving accuracy while reducing real-time resource consumption
Solution Approach 2:
The system changes parameters by dynamically adjusting model parameters based on user profile data and transaction characteristics. The allocating data model uses adjustable parameters such as adjustment unit counts, skew parameters, and plateau parameters that are modified according to the specific user and item context, allowing the model to adapt to different scenarios and improve selection accuracy without requiring completely new models for each case
2Ease of operation
If conventional data models generate transmissions and interfaces, then networking and memory resources are used, but user engagement with the generated content is low
Solution Approach 1:
The system applies local quality by customizing transmissions and interfaces according to specific user characteristics and item properties. The allocating data model generates user-specific incentive pricing recommendations tailored to individual user profiles rather than using generic approaches. This personalization makes the content more relevant and engaging for each user, improving interaction rates while reducing the need to generate and transmit大量 generic content that would consume networking resources
3Measurement precision
If conventional data models are used for item selection, then computing resources are consumed, but the accuracy of generating user-likely offers is insufficient
Solution Approach 1:
The system segments the item selection and offer generation process into distinct components: user profile data aggregation, adjustment unit count determination, electronic item representation identification, and allocating data model application. Each segment can be independently optimized and cached. By dividing the complex process into manageable segments, the system improves accuracy through specialized processing in each stage while reducing overall computing resource requirements through efficient resource allocation across segments
Data Source
AI summary
Embodiments of the present disclosure provide for improved determination of adjustment unit allocation(s). For a particular user profile and electronic item representation, embodiments determine a recommended adjustment unit allocation particular to a transaction for that user profile and particular electronic item representation that increases and/or maximizes a particular goal metric while simultaneously maintaining or minimizing the decreased likelihood for a user to initiate such a transaction, or maximizing the likelihood that the user will initiate such a transaction. Embodiments of the present disclosure utilize allocation models specially configured for each user and prospective transaction for a particular electronic item representation.