A Focus on Female Consumer Review-Based Recommender Systems

In today's e-commerce world, personalized recommendation systems are crucial for enhancing user experience and increasing sales. This research focuses on developing a recommendation system based on female consumer reviews, aiming to improve the quality of recommendations for women's fashion products by analyzing user feedback. We use advanced Natural Language Processing (NLP) techniques combined with deep learning models to comprehensively analyze online reviews of women's goods. This approach not only parses the emotional tendencies of the review content, but also identifies key product features mentioned in the reviews, such as style, size, and material. Our system is able to more accurately understand consumer preferences and needs through these analyses. On this basis, we further combine the product feature data in order to achieve more personalized recommendations.

I. Introduction

The advent of e-commerce has revolutionized the way consumers shop, offering convenience, variety, and accessibility[8]. However, with the explosion of online product offerings, customers are often overwhelmed with choices. In this context, recommendation systems have emerged as a pivotal tool to guide consumers in their purchasing journey, enhancing user experience and engagement. Particularly in the fashion industry, where personal taste and fit play significant roles, the need for more sophisticated, personalized recommendation systems is more pressing than ever. This study zeroes in on the e-commerce sector of women's fashion, a domain where consumer choices are influenced heavily by individual preferences, trends, and product reviews. Women's fashion e-commerce presents unique challenges and opportunities due to the diverse range of products and the subjective nature of style and fit. Recognizing this, our research aims to develop a recommendation system that leverages natural language processing (NLP) to analyze female consumer reviews. By interpreting these reviews, the system can uncover rich insights into customer preferences and sentiments. [9], [10]