Product Categorization for Retail LLM

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Project Overview:

Objective

The goal is to build a comprehensive dataset that enables LLMs to accurately understand and categorize a wide variety of retail products, improving the user experience in e-commerce platforms.

Scope

The dataset includes diverse product descriptions across multiple retail categories such as electronics, fashion, and home goods. The categorization aims to ensure that products are easily searchable and recommendation accurately base on user preferences.

Sources

Online Retail Platforms: Product descriptions were sourced from various online retail platforms, covering a wide range of categories.
Category Annotation: The descriptions were systematically categorized into predefined categories to enhance consistency and relevance in product categorization.

Data Collection Metrics

Total Product Descriptions Collect: 50,000 product descriptions.
Categories Annotated: 100,000 annotations, with each product categorized into two distinct categories.

Annotation Process

Stages

Category Assignment: Each product description was analyzed and assigned to one of 100 predefined categories. This dual categorization was implemented to improve the LLM’s ability to understand and classify products with greater accuracy.
Consistency Checks: Regular checks were conducted to ensure the accuracy and relevance of the assigned categories.

Annotation Metrics

Total Categories Annotated: 100,000 categories, with a dual categorization approach applied to each product.
Annotator Team Size: A team of 40 annotators collaborated on the project.

Quality Assurance

Stages

Consistency Validation: Continuous validation processes were in place to ensure that the annotations were accurate and consistent with the predefined categories.
Data Relevance: The dataset was regularly review to ensure that it remain relevant to the evolving needs of online retail platforms.
Duration: The project was complete within 2 months, ensuring timely delivery while maintaining high-quality standards.

QA Metrics

Categorization Accuracy: The project achieve a high categorization accuracy, ensuring that products were correctly classify into relevant categories.
Relevance Check: Continuous feedback was incorporate to improve the relevance and accuracy of the dataset.
Timeliness: The project was completed within the planned time frame of 2 months, ensuring no delays in deployment.

Conclusion

The creation of this comprehensive dataset marks a significant advancement in the ability of LLMs to categorize retail products accurately. This enhancement directly contributes to better search accuracy and more relevant product recommendations in e-commerce platforms, ultimately improving the user experience.