Sherwin Williams Recommendation System

Publication Date

Spring 4-22-2026

Presentation Length

Poster/Gallery presentation

College

College of Sciences & Mathematics

Department

Math and Computer Science, Department of

Student Level

Undergraduate

Faculty Mentor

Christina Davis

Presentation Type

Poster

Summary

For decades, lead-based paint served as a widely used standard in the paint industry due to its affordability and durability. However, over time, exposure to lead was linked to serious health risks, including neurological damage and other long-term complications. In response, the United States banned the residential use of lead-based paint through the Consumer Product Safety Commission in 1978.

Following this shift, the paint industry evolved toward safer formulations and introduced a wider range of specialized products, including low-VOC paints, antimicrobial coatings, and stain-blocking technologies. While these innovations improved safety and performance, they also significantly increased the complexity of product selection.

As a result, modern homeowners are often faced with an overwhelming number of paint options. Although product data sheets provide detailed information, interpreting these specifications and selecting the most appropriate product for a given project remains a challenge.

This project develops a data-driven recommender system that utilizes publicly available Sherwin-Williams product data to assist users in selecting appropriate paint products based on project requirements and constraints. By structuring and filtering key product attributes, the system aims to streamline decision-making and improve accessibility to product knowledge.

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