Active Sampling Predictor Adaptation for Product Search
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Solution Overview
Problem
Existing systems face challenges in adapting predictors trained on one domain to perform well on another domain with different input and conditional output distributions, leading to poor performance due to underlying changes in distributions, such as in spam mail classification and online retailer product prediction across different categories.
Innovation Solution
The ASTraL framework employs a Multi-Armed Bandit Active Sampling (MAB-AS) strategy to identify and actively sample from disagreement clusters, using a domain discriminator to determine agreement and disagreement regions, and adapt the predictor to improve performance on the target domain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a predictor is trained on source domain data, then the predictor can be applied to target domain tasks, but the predictor performs poorly due to distribution differences between domains
Solution Approach 1:
The system performs preliminary clustering of target domain data into agreement and disagreement clusters before training the final predictor. By pre-identifying disagreement clusters where source and target domains differ most, the system can focus adaptation efforts on the most critical regions, improving final prediction accuracy while maintaining adaptability across domains.
Solution Approach 2:
The system applies different treatment to different regions of the target domain by identifying agreement clusters (where source domain knowledge transfers well) and disagreement clusters (where adaptation is needed). This local differentiation allows the system to leverage source domain data effectively while focusing computational resources on problematic regions, resolving the contradiction between adaptability and accuracy.
2Reliability
If active sampling is performed in disagreement clusters, then prediction accuracy on target domain improves, but computational complexity and sampling cost increase
Solution Approach 1:
The system segments the target domain into multiple clusters based on similarity to source domain, specifically identifying disagreement clusters where distribution differences are most pronounced. This segmentation allows focused active sampling only in critical regions rather than uniform sampling across the entire target domain, improving accuracy while controlling computational complexity through targeted intervention.
Solution Approach 2:
The system introduces a domain discriminator as an intermediary component that measures distributional distance between source and target domains. This intermediary enables intelligent identification of disagreement clusters, allowing the active sampling mechanism to target only the most problematic regions, thereby improving prediction accuracy without uniformly increasing complexity across all data processing operations.
3Quantity of substance
If uniform sampling is performed across target domain, then coverage is improved, but performance on specific disagreement regions deteriorates
Solution Approach 1:
The system changes the sampling parameter from uniform distribution to non-uniform distribution weighted by cluster disagreement metrics. By modifying the sampling distribution to oversample disagreement clusters and undersample agreement clusters, the system achieves better performance on critical regions while maintaining adequate overall coverage, effectively resolving the contradiction between quantity and quality of sampled data.
Data Source
AI summary
Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of training a source classifier with labeled source training data of a first product category from a website of an online retailer, clustering target data for a second product category into a plurality of clusters, inserting into each cluster labeled source training data of the first product category, assigning a domain discriminator score to each cluster, determining whether each cluster comprises an agreement cluster or a disagreement cluster using the domain discriminator score, receiving a product search request for a product of the second category from a user of the web site, and coordinating a display of the product on the web site to promote the product.