Product recommendation approach based on quality house and user profile

Abstract

To address the issues of neglected review information and insufficient personalization in product recommendations, we developed a recommendation approach combining Quality Function Deployment (QFD) with user profiling. Our method employs data mining and TF-IDF algorithms to extract product feature words from consumer reviews, constructs preference vectors for product attributes and builds both “consumer-product attribute” and “product-product attribute” house of quality models. Additionally, we incorporate user profile labels to optimize recommendation rankings. Validation using lipstick review data from the Tmall platform confirms the model's feasibility. Results demonstrate that this approach offers a novel solution to the personalized ranking challenges while expanding the application scope of Quality Function Deployment (QFD), providing a foundation for developing interpretable recommendation systems.