Date of Award

6-2025

Degree Name

MS in Industrial Engineering

Department/Program

Industrial and Manufacturing Engineering

College

College of Engineering

Advisor

Puneet Agarwal

Advisor Department

Industrial and Manufacturing Engineering

Advisor College

College of Engineering

Abstract

With the considerable presence of e-commerce in society, vast number of purchasable goods, and increasing brand variety, consumers are faced with the challenge of buying products that they perceive to be of greatest value to them. To assist consumers with making better informed decisions, e-commerce websites allow individuals to post their own experiences and score the products that they purchase. Despite this information, the variety of experiences and feedback that consumers share do not always lead to clarity on whether a product is best suited for the purchaser. To help guide customers through a simplified purchasing process from the perspective of an online retailer, this study seeks to explore a possible framework for combining existing quantitative product metrics and qualitative reviews with a recommender system. Results are then compared with recommendations provided by the well-known ChatGPT language model, which is also emerging as a trusted decision-making system. 10 different product categories, each containing 8 to 10 products, had their customer review data aggregated and analyzed for quantitative sentiment scores. The resulting data was mixed with common numerical product attributes and analyzed with common multi-criteria decision making models to form organized product recommendations. The final recommendations demonstrated some discrepancies from ChatGPT. However, our framework demonstrated that a sentiment-inclusive recommender system can be established with similar performance to complex models such as ChatGPT, and that further experimentation on feature selection could improve recommendation performance.

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