Asset Similarity Filtering for ML Experiment Convergence
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Solution Overview
Problem
Online retail websites face a computational burden in identifying optimal combinations of item assets, such as images and textual data, for item detail pages, as existing methods like multi-armed bandit algorithms are time-consuming and resource-intensive due to the inclusion of similar assets that do not produce statistically significant results.
Innovation Solution
A machine-learning model is used to identify feature vectors for item assets, calculating similarity scores to filter out similar assets, reducing the number of assets in experiments and accelerating the identification of the optimal combination through a user interface that highlights and allows removal of redundant items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-armed bandit algorithms are used to identify optimal item asset combinations, then experimental coverage is improved, but computational burden and time to converge increase significantly
Solution Approach 1:
The system performs preliminary clustering of item assets into canonical groups before conducting experiments. By pre-organizing assets into canonical item asset groups based on similarity analysis, the system reduces the search space for multi-armed bandit algorithms, enabling faster convergence while maintaining comprehensive experimental coverage across diverse asset types.
Solution Approach 2:
The system creates canonical representations that capture the essential characteristics of similar item assets. Instead of treating each unique asset variant separately, the system uses canonical models to represent groups of similar assets, reducing redundancy in experiments while preserving the ability to evaluate meaningful variations in asset combinations.
2Reliability
If all item assets are included in experiments, then comprehensive evaluation is achieved, but computational resources are wasted on similar assets that produce statistically insignificant results
Solution Approach 1:
The system extracts and removes redundant similar assets from the experiment pool by clustering them into canonical groups. By identifying and eliminating duplicate or highly similar asset variants, the system reduces computational waste while maintaining evaluation of diverse and meaningful asset combinations through selective inclusion of representative canonical assets.
Solution Approach 2:
The system changes the parameter of asset representation from individual unique assets to canonical group representatives. By transforming the experiment space from evaluating every individual asset variant to evaluating canonical representations, the system reduces the number of experimental configurations needed while maintaining comprehensive coverage of meaningful asset diversity.
3Measurement precision
If numerous item assets are tested in experiments, then optimal combination accuracy is improved, but the number of required experiments increases exponentially
Solution Approach 1:
The system segments the large set of item assets into smaller canonical groups based on similarity metrics. By dividing the asset space into manageable canonical segments, the system reduces the combinatorial explosion of possible experiments while maintaining the ability to accurately identify optimal combinations within and across segments through structured experimentation.
Solution Approach 2:
The system performs preliminary clustering and canonical asset identification before conducting combination experiments. This pre-processing step organizes assets into structured groups, enabling more efficient experiment design that evaluates canonical combinations rather than all possible individual asset permutations, thereby improving throughput while maintaining accuracy.
Data Source
AI summary
Systems and methods are described herein for reducing the computational burden related to performing one or more experiments. The set of item assets (e.g., images, text, features, descriptions, etc.) may be reduced in an intelligent manner to enable the set to include more disparate assets. The system may obtain vectors that describe each asset. A similarity score (or other indication/representation of similarity) may be presented for each pair of assets and displayed at a user interface. Using the similarity scores (or similarity representations) as a guide, the user may reduce the set of assets. The reduced set of assets may then be utilized to perform one or more experiments in order to identify an optimal selections from the assets. In some embodiments, the one or more experiments may utilize an explore/exploit algorithm (e.g., a multi-armed bandit algorithm) to identify an optimal selection of item assets.