Fashion Trend forecasting with machine learning:predicting consumer preferences for personalized recommendtion

Abstract

 Fashion trend forecasting plays a pivotal role in the dynamic and ever-evolving landscape of the fashion industry. Leveraging machine learning techniques, this study explores data-driven approaches to analyze and predict consumer preferences, facilitating the development of personalized recommendations. The research encompasses a comprehensive process, including data collection from various sources such as historical sales records, customer reviews, and social media trends. Feature engineering techniques are employed to extract relevant attributes, and sentiment analysis is applied to decipher consumer sentiments from reviews and social media data.

Image recognition methods are utilized to interpret fashion images, identifying emerging trends and popular styles. Collaborative filtering algorithms, both user-based and item-based, are implemented to discern patterns and similarities in user preferences. Content-based filtering systems are developed to recommend items based on user behavior and item characteristics. Deep learning models, such as neural networks, are explored to capture intricate patterns in consumer behavior and fashion trends.

Time series analysis is incorporated to understand temporal aspects of fashion preferences and predict how trends evolve over time. The recommendation system is personalized by considering individual user preferences, browsing history, and purchase behavior. A/B testing is employed to assess the system’s performance, comparing recommendations against a control group to measure user engagement and satisfaction.

A continuous feedback loop is established to improve the model iteratively, monitoring user interactions and incorporating feedback to adapt to changing fashion trends. Interpretability is emphasized to build user trust, ensuring transparency in the reasoning behind specific recommendations. Ethical considerations related to data privacy and bias are addressed to uphold fairness and inclusivity. The final model is deployed in a production environment, providing real-time personalized recommendations to users.

This research contributes to the advancement of fashion trend forecasting by integrating machine learning methodologies into the decision-making process, enhancing the ability to anticipate and meet consumer preferences in a rapidly changing fashion landscape.

DOI - https://doi.org/10.65525/SVUP.9788199565418.2026.127-137