Online Parameter Estimation for Dynamic Regression Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Network-based media systems face significant computational challenges in accurately predicting user interactions and optimizing user interface presentations due to the large volume of data required for accurate predictions, which is difficult to process in real-time.
Innovation Solution
The implementation of online algorithms that allow new information to be added to predictive models without reprocessing existing data, enabling efficient updates and reducing computational load by selecting the most effective user interface instances based on probabilistic models and variance estimation in dynamic regression models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large volume of data is collected to increase prediction accuracy, then measurement precision is improved, but computational load and processing time increase significantly
Solution Approach 1:
The patent pre-computes and stores statistical properties (mean, variance, higher moments) of feature distributions during an offline phase. These pre-computed statistics are then used during online prediction to avoid reprocessing the entire dataset, thus achieving high prediction accuracy without the computational burden of processing large volumes of data in real-time.
Solution Approach 2:
The patent extracts and utilizes only the most critical statistical characteristics (first four moments) of the data distribution for making predictions. By focusing on these key parameters rather than processing all raw data points, the system achieves accurate predictions with significantly reduced computational complexity.
2Measurement precision
If existing data is reprocessed to incorporate new information, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system pre-computes and stores summary statistics of historical data during an offline processing phase. When new data arrives, the system uses these pre-computed statistics combined with new observations to update predictions immediately, avoiding the need to reprocess the entire historical dataset and thus eliminating time loss while maintaining model accuracy.
Solution Approach 2:
The patent implements dynamic prediction models that can incorporate new information incrementally. The model updates its parameters by combining pre-computed statistical properties with new observations, allowing the system to adapt to changing conditions in real-time without reprocessing historical data, thus achieving both accuracy improvement and minimal time loss.
Data Source
AI summary
A system of assessing deployments in a network-based media system is provided herein. The system includes a data storage system storing observation vectors, each observation vector being associated with an outcome indicator, and a processing device in communication with the data storage system to receive and store observation vectors and associated outcome indicators. The processing device performs operations including communicating with an endpoint device of a user to obtain information associated with the endpoint device; and transmitting an instance of a variable user interface to the endpoint device for presentation to the user via the endpoint device based on the stored observation vectors, the stored associated outcome indicators, and the obtained information associated with the endpoint device. Related systems and methods are also disclosed.