Adaptive Bid Value Generation for Tail Digital Content Objects
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Solution Overview
Problem
Generating an electronic bid value for tail digital content objects is challenging due to the lack of historical data, leading to inefficient use of computing resources and potential sub-optimal bid values, which can impact the success of digital content object campaigns.
Innovation Solution
A computing entity uses a machine-learning model to iteratively retrieve and expand data clusters based on geolocation and category until a sufficient data records count is met, then generates an electronic bid value using historical interaction currency values from the final cluster.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional bid value generation methods are used for tail digital content objects, then bid values can be generated, but the accuracy is sub-optimal due to lack of historical data
Solution Approach 1:
The patent introduces an intermediary data cluster generation process that bridges the gap between limited historical data and bid value requirements. Related digital content objects are identified and clustered to create synthetic historical data structures, serving as a mediator that enables accurate bid value generation without requiring extensive direct historical data for the tail digital content object itself
Solution Approach 2:
The system performs preliminary actions by pre-generating data clusters from related digital content objects before bid value generation is needed. This preliminary data preparation creates a foundation of historical interaction patterns that can be leveraged immediately when bid values are required, eliminating the need to wait for sufficient historical data to accumulate
2Quantity of substance
If data clusters are expanded to include more related digital content objects, then more historical data becomes available, but computational resources and time increase
Solution Approach 1:
The patent applies partial action by retrieving only the necessary number of data clusters required to meet a minimum data threshold, rather than exhaustively processing all possible related digital content objects. This selective approach ensures sufficient data is gathered without unnecessary computational overhead from excessive data collection
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring the data records count during cluster retrieval and expansion. When the minimum data threshold is achieved, the process automatically stops expanding clusters, using feedback information to balance data sufficiency with computational efficiency
3Measurement precision
If the minimum data records count threshold is set higher, then bid value accuracy improves, but the time and resources required to gather sufficient data increase
Solution Approach 1:
The patent dynamically adjusts the minimum data records count threshold parameter based on the specific characteristics of the tail digital content object and the availability of related data. This parameter optimization ensures the threshold is set high enough for accuracy but not so high as to cause excessive data gathering delays, adapting to different scenarios
Data Source
AI summary
Embodiments of the present disclosure provide methods, systems, apparatuses, and computer program products for adaptively generating an electronic bid value for a tail digital content object.