Forecast Model for Network Content Change Impact
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Solution Overview
Problem
Traditional experimental systems for evaluating changes at network accessible sites require lengthy periods, often lasting several months or even a year, to determine the long-term impact on user behavior, which is resource-intensive and slows down the pace of innovation.
Innovation Solution
Integrating a long-term forecast model that uses short-term experiment data to predict the 365-day impact of changes, allowing for earlier decision-making on implementing changes and reducing the need for prolonged experiments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional A/B experiments are run for statistical significance, then measurement precision of user behavior impact is improved, but loss of time and productivity deteriorate due to experiments lasting 3-12 months
Solution Approach 1:
The system performs preliminary actions by collecting and storing user interaction data, device characteristics, and contextual information before the experiment begins. This pre-collected data serves as the foundation for rapid analysis, eliminating the need for long-duration experiments while maintaining measurement precision.
Solution Approach 2:
A machine learning model acts as an intermediary between the experimental data and the impact assessment. The model processes short-term experiment data combined with historical data to predict long-term user behavior impact, bridging the gap between brief experiments and comprehensive measurement.
2Reliability
If traditional A/B experiments are run for statistical significance, then reliability of user behavior impact assessment is improved, but productivity deteriorates due to delayed decision-making
Solution Approach 1:
The machine learning model serves as an intermediary that processes experimental results and historical data to generate reliable impact assessments rapidly. This intermediary enables both high reliability through comprehensive data analysis and high productivity through automated, fast processing.
Solution Approach 2:
The system replaces the mechanical, time-consuming process of long-duration A/B testing with an automated computational system. The machine learning model automatically analyzes data and generates reliable assessments, substituting the traditional sequential experimental process with parallel computational processing.
3Loss of time
If short-term experiments are used to speed up decision-making, then loss of time is reduced, but measurement precision of long-term user behavior impact deteriorates
Solution Approach 1:
The system merges short-term experimental data with long-term historical data to achieve both rapid decision-making and accurate long-term impact prediction. By combining these data sources, the model captures both immediate experimental effects and longer-term behavioral patterns.
Solution Approach 2:
The machine learning model acts as an intermediary that synthesizes short-term experiment results with historical patterns to predict long-term impact accurately. This intermediary enables the system to overcome the limitation of short experiment durations while maintaining measurement precision.
4Measurement precision
If comprehensive user data is collected and processed, then measurement precision and reliability are improved, but use of energy and computational resources worsen
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing user data in structured formats during normal operations. This pre-organization of data reduces the computational burden during experiment analysis, maintaining measurement precision while lowering energy consumption during the actual prediction process.
Solution Approach 2:
The system extracts only the most relevant features and characteristics from comprehensive user data for the machine learning model. By selecting key predictors rather than processing all raw data, the system maintains prediction accuracy while significantly reducing computational energy requirements.
Data Source
AI summary
Technologies are disclosed for determining network-accessible content changes based on computed models and providing a long term forecast of user interaction at a network accessible site based upon a short term experiment at the site. A forecast model for a period of time is generated based upon historical data of user interactions at the site. An experiment is run for a short term at the site based upon a potential change at the site. Based upon data obtained during the experiment, scores are generated for a control group (no change) and a treatment group (potential change) and compared. If there are statistically significant differences between the control group and the treatment group scores, the long term forecast may be used to forecast what the long term impact of the experiment would be based upon the short term experiment.