Machine Learning Retraining via Visual Similarity Ranking
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Solution Overview
Problem
Machine learning algorithms often suffer from inaccuracies due to insufficient or non-representative training data, leading to issues such as overprediction or underprediction, especially in applications like automated trading or search engine marketing.
Innovation Solution
A system and method for retraining machine learning algorithms using a visual similarity model, which involves training a visual similarity model, determining nearest neighbors, ranking them, re-ranking using a learning-to-rank algorithm, and updating the graphical user interface based on the re-ranked neighbors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If more training data is gathered to improve model accuracy, then prediction accuracy is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary actions by gathering diverse data sources (social media, news, forums, search queries) and pre-processing them into training data before the actual model training is needed. This advance preparation reduces the time required during urgent model retraining scenarios while maintaining high prediction accuracy through comprehensive data collection.
Solution Approach 2:
The training data is segmented into multiple diverse sources including social media data, news data, forum data, and search query data. Each source is processed independently and then combined, allowing parallel processing and reducing overall time consumption while improving the representativeness and accuracy of the training data.
2Loss of time
If pre-trained models are used to reduce training time, then time consumption is reduced, but model accuracy deteriorates due to outdated or biased training data
Solution Approach 1:
The system implements continuous retraining of the machine learning model using freshly gathered data from multiple sources. Instead of relying on static pre-trained models, the system continuously updates the model with current data, maintaining both speed (through automated pipelines) and accuracy (through up-to-date training data that reflects current conditions).
Solution Approach 2:
The system incorporates feedback mechanisms where model performance is continuously monitored and evaluated. Based on performance metrics and changing data patterns, the system automatically triggers retraining cycles, creating a feedback loop that maintains high prediction accuracy without requiring manual intervention, thus balancing speed and accuracy.
3Reliability
If extensive training data is collected to improve model reliability, then model reliability is improved, but device complexity increases
Solution Approach 1:
The system uses a unified data collection framework that handles multiple data sources (social media, news, forums, search queries) through a common architecture. This multi-functional system gathers, processes, and integrates diverse data types using the same pipeline, reducing system complexity compared to having separate systems for each data source while maintaining high model reliability through comprehensive data coverage.
4Loss of time
If purchased training data is used to avoid data gathering time, then time consumption is reduced, but cost increases and data quality deteriorates
Solution Approach 1:
The system performs self-service by automatically gathering, processing, and preparing training data from multiple public and private sources without requiring external vendors. The automated data collection and processing pipeline reduces both time consumption and costs while ensuring data quality through controlled processing steps and validation mechanisms.
Data Source
AI summary
A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising: training a visual similarity model to output one or more vector representations in a vector space; determining one or more nearest neighbors based on embeddings nearest to a respective embedding in the vector space for the item; ranking the one or more nearest neighbors for the item in an order; re-ranking the order of the one or more nearest neighbors of the item into a re-ranked order; inserting, into a display of the item, the one or more nearest neighbors of the item; and facilitating altering a graphical user interface (GUI) to convey the display of the item. Other embodiments are disclosed herein.