Relevant bibliographies by topics / Personalized Fashion

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Personalized Fashion'

Author: Grafiati
Published: 6 June 2025
Last updated: 9 June 2025

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Journal articles on the topic "Personalized Fashion"

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Vattikonda, Navya, Anuj Kumar Gupta, Achuthananda Reddy Polu, Bhumeka Narra, and Dheeraj Varun Kumar Reddy Buddula. "Leveraging Deep Learning for Personalized Fashion Recommendations Using Fashion MNIST." International Journal of Multidisciplinary Research in Science and Business 1, no. 07 (January 1, 2024): 54–77. https://doi.org/10.63665/ijmrsb.v1i07.08.

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People are particularly consciousof their clothing choices since fashion has a big influence on daily life. Large populations are usually recommended fashion goods and trends by specialists via a manual, curated process. On the other hand, e-commerce websites greatly benefit from automatic, personalized recommendation systems, which are becoming more popular. This study introduces a deep learning-based framework for personalized fashion recommendation, utilizing the Fashion-MNIST dataset as the primary data source. The dataset was dividedinto training and testing sets in a 70:30 ratio to ensure robust evaluation. CNN, Feedforward Neural Networks (FNN), and LSTM models were employed for fashion item classification. Evaluation metrics such as F1-score, recall, accuracy, precision, and loss, along with confusion matrix analysis, were utilized to assess model performance. Among the tested models, the CNN demonstrated superior performance, achieving 93.99% accuracy, with F1-score, recall, and precision all at 94% and a loss value of 0.2037. Comparative analysis further highlighted the CNN's effectiveness over FNN and LSTM models. These findings demonstrate the promise of CNN architectures for improving the precision and consistency of individualized clothing recommendation systems.
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Vattikonda, Navya, Anuj Kumar Gupta, Achuthananda Reddy Polu, Bhumeka Narra, and Dheeraj Varun Kumar Reddy Buddula. "Leveraging Deep Learning for Personalized Fashion Recommendations Using Fashion MNIST." International Journal of Multidisciplinary Research in Science and Business 1, no. 07 (January 1, 2024): 54–77. https://doi.org/10.63665/ijmrsb.v1i01.08.

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People are particularly consciousof their clothing choices since fashion has a big influence on daily life. Large populations are usually recommended fashion goods and trends by specialists via a manual, curated process. On the other hand, e-commerce websites greatly benefit from automatic, personalized recommendation systems, which are becoming more popular. This study introduces a deep learning-based framework for personalized fashion recommendation, utilizing the Fashion-MNIST dataset as the primary data source. The dataset was dividedinto training and testing sets in a 70:30 ratio to ensure robust evaluation. CNN, Feedforward Neural Networks (FNN), and LSTM models were employed for fashion item classification. Evaluation metrics such as F1-score, recall, accuracy, precision, and loss, along with confusion matrix analysis, were utilized to assess model performance. Among the tested models, the CNN demonstrated superior performance, achieving 93.99% accuracy, with F1-score, recall, and precision all at 94% and a loss value of 0.2037. Comparative analysis further highlighted the CNN's effectiveness over FNN and LSTM models. These findings demonstrate the promise of CNN architectures for improving the precision and consistency of individualized clothing recommendation systems.
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Navya, Vattikonda. "Leveraging Deep Learning for Personalized Fashion Recommendations Using Fashion MNIST." International Journal of Multidisciplinary Research in Science and Business 01, no. 07 (April 3, 2025): 1–8. https://doi.org/10.5281/zenodo.15130896.

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—People are particularly conscious of their clothingchoices since fashion has a big influence on daily life. Largepopulations are usually recommended fashion goods and trendsby specialists via a manual, curated process. On the other hand,e-commerce websites greatly benefit from automatic,personalized recommendation systems, which are becomingmore popular. This study introduces a deep learning-basedframework for personalized fashion recommendation, utilizingthe Fashion-MNIST dataset as the primary data source. Thedataset was divided into training and testing sets in a 70:30 ratioto ensure robust evaluation. CNN, Feedforward NeuralNetworks (FNN), and LSTM models were employed for fashionitem classification. Evaluation metrics such as F1-score, recall,accuracy, precision, and loss, along with confusion matrixanalysis, were utilized to assess model performance. Among thetested models, the CNN demonstrated superior performance,achieving 93.99% accuracy, with F1-score, recall, and precisionall at 94% and a loss value of 0.2037. Comparative analysisfurther highlighted the CNN's effectiveness over FNN andLSTM models. These findings demonstrate the promise of CNNarchitectures for improving the precision and consistency ofindividualized clothing recommendation systems
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Navya, Vattikonda Anuj Kumar Gupta Achuthananda Reddy Polu Bhumeka Narra and Dheeraj Varun Kumar Reddy Buddula. "Leveraging Deep Learning for Personalized Fashion Recommendations Using Fashion MNIST." International Journal of Multidisciplinary Research in Science and Business 1, no. 7 (April 6, 2025): 54–77. https://doi.org/10.5281/zenodo.15349661.

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People are particularly conscious of their clothing choices since fashion has a biginfluence on daily life. Large populations are usually recommended fashion goodsand trends by specialists via a manual, curated process. On the other hand, ecommerce websites greatly benefit from automatic, personalized recommendationsystems, which are becoming more popular. This study introduces a deep learningbased framework for personalized fashion recommendation, utilizing the FashionMNIST dataset as the primary data source. The dataset was divided into trainingand testing sets in a 70:30 ratio to ensure robust evaluation. CNN, Feed forwardNeural Networks (FNN), and LSTM models were employed for fashion itemclassification. Evaluation metrics such as F1-score, recall, accuracy, precision, andloss, along with confusion matrix analysis, were utilized to assess model performance. Among the tested models, the CNN demonstrated superiorperformance, achieving 93.99% accuracy, with F1-score, recall, and precision all at94% and a loss value of 0.2037. Comparative analysis further highlighted theCNN's effectiveness over FNN and LSTM models. These findings demonstrate thepromise of CNN architectures for improving the precision and consistency ofindividualized clothing recommendation systems. 
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M Vinitha, Dr.B. Nagarajanaik, Mallikarjuna Nandi, C Naga Sri Charan, and K Priyanka. "Fashion Recommendation System." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 05 (May 21, 2024): 1243–47. http://dx.doi.org/10.47392/irjaeh.2024.0171.

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Fashion recommendation systems have become increasingly essential in the e-commerce industry, providing personalized outfit suggestions to users, enhancing their shopping experience, and boosting sales. This paper presents a novel approach to fashion recommendation by combining machine learning and deep learning techniques. We leverage a comprehensive dataset of user preferences and fashion items to create a robust recommendation system. Our approach first employs collaborative filtering and matrix factorization methods to establish user-item interactions. Subsequently, deep learning models, such as neural collaborative filtering and recurrent neural networks, are utilized to capture intricate patterns within the fashion data. This combination enables the system to offer personalized fashion recommendations based on the user's historical choices, style, and real-time Behaviour. The evaluation of our system demonstrates its effectiveness in enhancing user engagement and satisfaction while increasing the platform's revenue. The proposed fashion recommendation system showcases the potential of integrating machine learning and deep learning for optimizing personalized fashion suggestions in the ever- evolving fashion e-commerce landscape. This research contributes to the broader field of recommendation systems and their applications in the fashion industry.
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Sangkhathat, Surasak. "‘Personalized’ or ‘Precision’, Future or Fashion." Journal of Health Science and Medical Research 36, no. 3 (August 14, 2018): 165. http://dx.doi.org/10.31584/jhsmr.2018.36.3.13.

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Thakur, Swapnil, Shreyas Dixit, and Prof Rahul Dagade. "Review of Personalized Outfit Recommender." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (November 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem26956.

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In the age of looking good, we come across that one question everyday; What should I wear to look good today?. The question span a broad variety of topics, such as what am I missing in wardrobe? what is the current trend? will this suit my skin tone? will this work in current climate. the integration of technology and clothing has revolutionized the way individuals perceive and choose their outfits. This study presents a cutting-edge outfit recommendation system that combines the analysis of user skin tones, real-time weather data, and advanced machine learning algorithms. The system utilizes a digital wardrobe to store a vast array of fashion items and employs a Convolutional Neural Network (CNN) to extract intricate features from these items, enhancing the accuracy of clothing recognition. To personalize recommendations, the system incorporates user skin tone analysis, ensuring that the suggested outfits complement the wearer's complexion. Additionally, real-time weather data is integrated to align outfit suggestions with the prevailing weather conditions, ensuring both style and comfort. The system utilizes K-Means clustering to categorize users based on similar fashion preferences and Decision Tree algorithms to refine outfit recommendations further. The proposed system not only enhances user experience but also contributes to sustainable fashion practices by promoting the optimal utilization of existing wardrobes, reducing impulsive shopping, and minimizing fashion waste. Through rigorous testing and validation, the system demonstrates its effectiveness in providing tailored outfit recommendations, thereby reshaping the future of personalized fashion choices Keywords: Apparel recommendation, digital wardrobe, Deep learning, Fashion e-commerce.
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Sun, Kexin, Peng Zhang, Jie Zhang, Jing Tao, and Kexin Yuan. "PFNet: Attribute-aware personalized fashion editing with explainable fashion compatibility analysis." Information Processing & Management 61, no. 1 (January 2024): 103540. http://dx.doi.org/10.1016/j.ipm.2023.103540.

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Paik, Hyo Yon, and Jee Hyun Lee. "Personalized Fashion Design using Data Visualization method." Journal of the Korean Society of Costume 67, no. 5 (August 31, 2017): 17–30. http://dx.doi.org/10.7233/jksc.2017.67.5.017.

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Sinha, Abhinav. "Style Craft: AI-Driven Fashion Platform." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (May 12, 2025): 1–9. https://doi.org/10.55041/ijsrem47571.

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1. ABSTRACT As artificial intelligence continues to reshape industries, personalized and intelligent systems are becoming essential for enriching digital experiences. Style Craft: AI-Driven Fashion Platform introduces a next-generation fashion assistant designed to redefine how users discover, interact with, and personalize their style choices. The platform delivers curated fashion recommendations, enables virtual outfit trials, and helps users stay updated with current trends through an intuitive and immersive interface. Built with Python and enhanced by cutting-edge AI methodologies, the system leverages computer vision, natural language understanding, and recommendation engines to offer dynamic suggestions tailored to individual preferences, body profiles, and browsing behavior. Core components include an AI-powered virtual try-on system, style compatibility analysis, and trend forecasting modules, all accessible through a responsive web interface. This paper details the system's architecture and the technologies that power it, emphasizing how AI elevates personalization, visual recognition, and interaction design in the fashion domain. It also addresses implementation challenges, including optimizing garment recognition, adapting to user variability, and maintaining fluid performance. Looking forward, the platform envisions broader capabilities such as conversational AI for voice-guided fashion navigation, AR/VR support for immersive try-ons, and integration with real-time retail inventories for seamless shopping. Style Craft underscores the innovative potential of AI in crafting tailored, engaging, and futuristic fashion experiences for modern users. ACM Reference Format: Mitansh Sehgal, Nikhil Maurya, Abhinav Sinha. 2025. Style Craft: AI-Driven Fashion Platform. Keywords – Artificial Intelligence, Personalized Recommendations, Fashion Technology, , Trend Forecasting, Recommendation Systems, User Personalization, Human-Computer Interaction, Conversational AI, Style Analysis, Intelligent Fashion Assistant, E-commerce Innovation
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Dissertations / Theses on the topic "Personalized Fashion"

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Wang, Lichuan. "Contribution to development of an intelligent system for supporting personalized fashion design." Thesis, Lille 1, 2012. http://www.theses.fr/2012LIL10033/document.

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La mass customisation a été appliquée au marché de grande consommation de vêtements depuis plus de 20 ans. Pourtant, les travaux concernés se focalisent essentiellement sur le prototypage virtuel par utilisation des outils de CAO. Le stylisme et le marketing n’ont pas été étudiés de façon systématique. Dans le cadre de ma thèse doctorale, nous proposons un système d’aide à la décision orienté vers les styles afin de fournir des conseils aux créateurs. Dans ce système, nous caractérisons, d’abord, les perceptions de créateurs et de consommateurs sur les morphotypes. Deux expériences ont été effectuées afin d’acquérir les données des experts (descripteurs sensoriels) décrivant les corps virtuels sans et avec styles de vêtements. Une autre expérience a été réalisée pour extraire les données des consommateurs sur les relations entre les thèmes (images socioculturelles souhaités) et les descripteurs sensoriels. Ensuite, ces données perceptuelles sont formalisées et analysées par utilisation des ensembles du flou, des arbres de décision, et des cartes cognitives floues. La modélisation des relations entre ces perceptions et les mensurations du corps permettent de calculer les degrés de pertinence d’un corps humain sans et avec style de vêtement par rapport à un thème spécifique. La comparaison de ces deux degrés de pertinence permet de déterminer si un nouveau style de création est faisable pour un thème donné. Le système proposé a été testé et analysé dans deux cas réels : la création des styles personnalisés et la sélection des styles pour un marché de grande consommation
Mass customization has been applied in fashion mass market for more than 20 years. However, the related work mainly focuses on application of CAD tools such as body shape modeling and garment modeling. Fashion design and fashion marketing have not been involved systematically. In fact, when developing mass customized products, we should study human perception on products, including consumer’s and design expert’s perception, and integrate it into the new process of design.In my PhD research project, we originally propose a fashion decision support system for supporting designer’s work. In this system, we first characterize and acquire fashion expert perception and consumer perception on human body shapes. Two experiments are proposed in order to acquire expert perceptual data (sensory descriptors) on naked virtual body shapes and those with garment design styles. Another experiment is carried out for acquiring consumer perceptual data on relations between fashion themes (images desired by general public) and sensory descriptors. Next, these perceptual data are formalized and analyzed using the intelligent techniques, i.e. fuzzy set theory, decision tree and fuzzy cognitive map. The complex relations between these perceptions as well as the physical measurements of body shapes are modeled, leading to compute the relevancy degrees of a naked body and a body with a garment design style to a given fashion theme. The comparison of these two relevancy degrees will permit to determine if a new design style is feasible or not for a given fashion theme. The proposed system has been tested and analyzed in two real cases: i.e. customized design and mass market selection
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Urrea, Inmaculada. "La Construcción de la marca personal de Coco Chanel a través de sus fotografías: su aportación a la creación de la mujer moderna." Doctoral thesis, Universitat Pompeu Fabra, 2016. http://hdl.handle.net/10803/385858.

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El objetivo principal de la presente tesis es investigar sobre cómo Coco Chanel, la mujer más importante de la creación de moda femenina y una de las mujeres más importantes del siglo XX, construyó su marca personal a través de sus retratos. Para ello se investiga en dos direcciones, una, la marca en todas sus dimensiones, desde la comercial a la antropomorfa, pasando por su relación con la emoción y el recuerdo, y los modelos de construcción de marca personal. Otra, la semiótica aplicada al campo visual para desentrañar todos los significados asociados al corpus fotográfico de Chanel analizado. Además, para enmarcar el tema central se analiza la aportación del personaje a la construcción de la mujer moderna, así como el relato construido sobre su persona, mediante los biógrafos que la conocieron, y sus aportaciones a la moda.
The main objective of this thesis is to investigate how Coco Chanel, the most important designer in the creation of women's fashion and one of the most important women of the twentieth century, built her personal brand through her portraits. For this matter, it has been researched in two different paths. First the brand in all dimensions, from the commercial to the anthropomorphic, through its relationship with emotion and memory, and models of personal brand building. Second semiotics applied to visual field, for unravel the meanings associated with the Chanel corpus analyzed through pictures. Furthermore, the study focuses on the legacy of her character to the construction of modern women and the stories built it on herself, as told by biographers who knew her, and Chanel’s contributions to fashion.
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Chiu, Yen-Lin, and 邱彥霖. "The Personalized Recommender System for Kiosk in Fast Fashion Industry." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/34596101502354736273.

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碩士
國立交通大學
交通運輸研究所
98
Because of the rapid development of information technology, many clothing enterprises try to implement the concept of smart store. These enterprises introduce a number of technology applications to their physical stores. In addition, these enterprises take “Fast Fashion” as their business strategy so that their clothing products would be diversity and have a quite short life cycle. The Kiosk is a new facility in physical clothing store and there is no recommendation system for Kiosk. The traditional recommendation system is not an efficient way for Kiosk because of the Cold Start and over-specialization problem. The Cold Start problem will decrease the performance of recommendation system. The over-specialization problem can only focus on some products while making recommendation for customers. In order to solve the problems mentioned above, the recommendation system proposed in this study analyzes the nature of clothing products. The proposed system tries to learn about customers’ preferences for the nature of products and make recommendations. We can reduce the impact of Cold Start problem with this approach. This study solves the over-specialization problem by making recommendation lists based on association rule method. This proposed recommendation system that combine data mining, collaborative filtering and content-based filtering would apply to Kiosk in fast fashion industry. In this study, the architecture of the recommendation system is more flexible. According to the data and purposes, this recommendation system is divided into four sub-modules such as the branch transactions, customer data, transaction data and customer interaction data. Users can constitute the structure from distinct modules at will to meet the requirement in different situations. To provide personalized recommendations, system will analyze historical transactions and interaction data among users to learn their preferences and behaviors and then predicts what kind of the products users need. Not only provide a personalized and meaningfully ordered list, enterprises can also deduce and develop marketing and sales strategy from the recommendation system in this study. For example, For example, in case of the operating costs is not a limitation, enterprises can hold some bundle sales by taking customer preferences and association rules as reference. This strategy may strengthen customer’s purchase intention and improve business operation performance.
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Books on the topic "Personalized Fashion"

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Hernandez, Helen. Manual de personalidad e imagen: Brilla con luz propia. México, D.F: Editorial Pax México, 2007.

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Abling, Bina. Fashion Sketchbook. Bloomsbury Publishing Plc, 2023. http://dx.doi.org/10.5040/9781501387920.

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Learn how to draw fashion images that communicate design ideas and details. With more than 3,000 color illustrations and updated instructions, the book shows you how to draw women, men, and children, pose the figure, develop the fashion head and face, sketch accessories, add garment details, and prepare flats and specs. Learn more advanced techniques for rendering color, fabrics, and embellishments, from houndstooth and velvet to feathers and fringe. Bina Abling’s detailed, easy-to-follow lessons have clear diagrams and runway photographs to help you develop your drawing skills. New to this Edition: -Discussion of sustainability as a mainstay in the fashion industry -Inclusion of practice templates -New videos in STUDIO to support student learning STUDIO Features Include: -Watch videos that bring chapter concepts to life -Study smarter with self-quizzes featuring scored results and personalized study tips -Review concepts with flashcards of essential vocabulary Instructor Resources -Instructor’s Guide provides suggestions for planning the course and using the text in the classroom -PowerPoint Slides for each chapter to help incorporate the text into the classroom
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Art, Black. Molly: Personalized Edgy Fashion Themed Journal with Lined Pages. Independently Published, 2018.

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Art, Black. Naomi: Personalized Edgy Fashion Themed Journal with Lined Pages. Independently Published, 2018.

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Harlacher, Petra. Personalized Fashion Teacher Gift: Beautiful Appreciation Gift for Women. Independently Published, 2021.

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Art, Black. Peyton: Personalized Edgy Fashion Themed Journal with Lined Pages. Independently Published, 2018.

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Ulasewicz, Connie, and Janet Hethorn. Sustainable Fashion. 3rd ed. Bloomsbury Publishing Inc, 2023. http://dx.doi.org/10.5040/9781501385650.

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Sustainable Fashion: Take Action, Third Edition presents a fresh exploration of practices that are underway in design and production within the fashion industry and the possibilities for future directions that can be taken now. This book focuses on innovative action needed to achieve the goal of creating healthier environments, reducing climate change, and improving the well-being of all people as they choose and wear clothing. This third edition continues to delve into the role that fashion plays in a sustainable future, through the interconnected model of “Connecting with People, Processes, and Environment”, which marks the focus of the book’s three sections. Covering a wide range of sustainability practices, the chapters are written by both academic and industry professionals, providing a balanced view of the topics with breadth and depth and suggesting routes for further examination. New to this Edition: -Thoroughly revised to cover advancements since the last edition, topics of equity, diversity, and inclusion are paramount within in each chapter, and social justice as a concept is highlighted throughout -Changes in cultural, social, and health contexts as they impact fashion action are spotlighted in every chapter -“Take Action” features are integrated within chapters STUDIO Features Includes: -Study smarter with self-quizzes featuring scored results and personalized study tips -Review concepts with flashcards of essential vocabulary Instructor Resources -Instructor’s Guide provides suggestions for planning the course and using the text in the classroom, supplemental assignments, and lecture notes -PowerPoint ® presentations include images from the book and provide a framework for lecture and discussion
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Rousso, Chelsea, and Nancy Kaplan Ostroff. Fashion Forward. 3rd ed. Bloomsbury Publishing Inc, 2024. http://dx.doi.org/10.5040/9781501374333.

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Fashion Forward, Third Edition demystifies the ever-changing career of fashion forecasting. It provides an overview of fashion forecasting theories and concepts, then gives a step-by-step guide to creating and presenting a forecast. The events of the 21st century have turned fashion on its head, forcing the industry to adapt to global pandemics, protests, and shifts in political power, and this book teaches readers to think critically about how the impacts of these events affect fashion trends. Different fashion consumers have different needs, and Fashion Forward considers a diverse range of consumers so readers can predict and satisfy their fashion needs. Examples and interviews from the industry professionals who inspire, create, manufacture, and market fashion help readers to start spotting and communicating tomorrow's trends today. New to this Edition -Discusses new technologies like interactive genetic algorithms and augmented and virtual reality and how they can be used in trend predictions -Focuses on a wider global perspective and focuses on inclusive, diversified cultures -Coverage of current events like the Covid-19 pandemic, social justice movements, sustainability, and more -Updated end-of-chapter activities and trend forecasts STUDIO Features Include: -Study smarter with self-assessment quizzes featuring scored results and personalized study tips -Review concepts with flashcards of terms and definitions -Learn up-to-date concepts with informative and accessible videos Instructor Resources Include: -Instructor’s Guide with Test Bank provides suggestions for planning the course and using the text in the classroom, supplemental assignments, and lecture notes -PowerPoint® presentations include images from the book and provide a framework for lecture and discussion
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Ellinwood, Janice Greenberg. Fashion by Design. 2nd ed. Bloomsbury Publishing Plc, 2022. http://dx.doi.org/10.5040/9781501359439.

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Fashion by Design, Second Edition, explains how the elements and principles of design relate to fashion, based on the philosophy of the Bauhaus Experiment of the 1920s and 1930s, which is the foundation for art education in the United States. The book is structured into three parts: the stages of the design process (inspiration, identification, conceptualization, exploration/refinement, definition/modeling, communication, and production); physical elements (such as line, shape, form, space, texture, light, pattern, color, and value); and theoretical principles (like balance, emphasis, rhythm, proportion, and unity) of design. This is reinforced by fashion designer profiles and illustrations covering art, architecture, and fashion. The book aims to improve the designer’s eye for creating fashion and related art forms; to identify terminology used in the communication of fashion; and to show how other factors, such as the human form, clothing structure, historic silhouettes, fashion trends, culture, and industry trends, may impact the development of a line or a collection. New to this Edition: - New introductory chapter on the stages of the design process - New chapter on sustainable design - New end-of-chapters exercises with application to the fields of fashion design (including the development of a design journal), fashion merchandising (such as styling, product development, buying or trend research) and theater arts (such as costume, sets, lighting) STUDIO Features: - Flashcards based on the glossary to enhance comprehension of key concepts and terms - Downloadable “Paper Dolls” pdfs for students to interact with key concepts of the design process - Study smarter with Self-Assessment Quizzes featuring scored results and personalized study tips Instructor’s Resources: - PowerPoint Slides for each chapter - Instructor’s Guide with sample course outlines for teaching and tools for integrating the STUDIO with the course
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Jalal Coloring Jalal Coloring Books. Personalized Wedding Coloring Book for Adults: Adult Coloring Book with Beautiful Wedding Dresses , Fashion and Features Wedding Fashion Illustrations. Independently Published, 2022.

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Book chapters on the topic "Personalized Fashion"

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Plumbaum, Till, and Benjamin Kille. "Personalized Fashion Advice." In Smart Information Systems, 213–37. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14178-7_8.

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Iliukovich-Strakovskaia, Anna, Victoria Tsvetkova, Emeli Dral, and Alexey Dral. "Non-personalized Fashion Outfit Recommendations." In Advances in Intelligent Systems and Computing, 41–52. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77700-9_5.

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Trakulwaranont, Donnaphat, Marc A. Kastner, and Shin’ichi Satoh. "Personalized Fashion Recommendation Using Pairwise Attention." In MultiMedia Modeling, 218–29. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98355-0_19.

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Guan, Weili, Xuemeng Song, Xiaojun Chang, and Liqiang Nie. "Heterogeneous Graph Learning for Personalized OCM." In Graph Learning for Fashion Compatibility Modeling, 89–108. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18817-6_6.

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Xu, Shihui, Jingyi Yuan, Xitong Sun, Yuhan Liu, Yuzhao Liu, Kelvin Cheng, Soh Masuko, and Jiro Tanaka. "Augmented Reality Fashion Show Using Personalized 3D Human Models." In Human Interface and the Management of Information. Designing Information, 435–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50020-7_31.

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Xian, Dan, Shaozan Cui, Bo Wang, and Lishuai Cui. "H&M Personalized Fashion Product Recommendation Using LightgbmRanker." In Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023), 201–8. Dordrecht: Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-198-2_23.

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Arunkumar, S., Gerard Deepak, J. Sheeba Priyadarshini, and A. Santhanavijayan. "PMFRO: Personalized Men’s Fashion Recommendation Using Dynamic Ontological Models." In Hybrid Intelligent Systems, 96–105. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27409-1_9.

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Ramampiaro, Heri, Helge Langseth, Thomas Almenningen, Herman Schistad, Martin Havig, and Hai Thanh Nguyen. "New Ideas in Ranking for Personalized Fashion Recommender Systems." In Business and Consumer Analytics: New Ideas, 933–61. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06222-4_25.

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Papachristou, Evridiki, Zoe Dimou, Margarita Grammatikopoulou, Lampros Mpaltadoros, and Thanos G. Stavropoulos. "Personalized Fashion On-Demand and e-Fashion Business Models: A User Survey in Greece." In Management and Industrial Engineering, 83–103. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98124-2_4.

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Bollacker, Kurt, Natalia Díaz-Rodríguez, and Xian Li. "Extending Knowledge Graphs with Subjective Influence Networks for Personalized Fashion." In Designing Cognitive Cities, 203–33. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00317-3_9.

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Conference papers on the topic "Personalized Fashion"

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Shoeb, Aamir, Mohammed Hamza Ali, Md Mahmood Ali, and Mohammad Sanaullah Qaseem. "PFRS: Personalized Fashion Recommendation System Using EfficientNet." In 2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), 337–44. IEEE, 2024. https://doi.org/10.1109/3ict64318.2024.10824272.

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Kumar, C. Siva, M. P. Deeraj, K. N. Harsha Vardhan, K. Amulya, and K. Govardhan. "Fashionista a Personalized Fashion and Style Recommendation System with Machine Learning Insights." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS), 1898–904. IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10717000.

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Yu, Cong, Yang Hu, Yan Chen, and Bing Zeng. "Personalized Fashion Design." In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00914.

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Stan, Cristiana, and Irina Mocanu. "An Intelligent Personalized Fashion Recommendation System." In 2019 22nd International Conference on Control Systems and Computer Science (CSCS). IEEE, 2019. http://dx.doi.org/10.1109/cscs.2019.00042.

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Gray, Chester, Meghan Beattie, Helena Belay, Sarah Hill, and Nicolette Lerch. "Personalized online search for fashion products." In 2015 Systems and Information Engineering Design Symposium. IEEE, 2015. http://dx.doi.org/10.1109/sieds.2015.7117018.

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Tu, Qingqing, and Le Dong. "An Intelligent Personalized Fashion Recommendation System." In 2010 International Conference on Communications, Circuits and Systems (ICCCAS). IEEE, 2010. http://dx.doi.org/10.1109/icccas.2010.5581949.

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Lu, Zhi, Yang Hu, Yunchao Jiang, Yan Chen, and Bing Zeng. "Learning Binary Code for Personalized Fashion Recommendation." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.01081.

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Li, Xingchen, Xiang Wang, Xiangnan He, Long Chen, Jun Xiao, and Tat-Seng Chua. "Hierarchical Fashion Graph Network for Personalized Outfit Recommendation." In SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3397271.3401080.

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Zeng, X., L. Koehl, L. Wang, and Y. Chen. "An intelligent recommender system for personalized fashion design." In 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS). IEEE, 2013. http://dx.doi.org/10.1109/ifsa-nafips.2013.6608496.

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Ding, Yujuan, P. Y. Mok, Yi Bin, Xun Yang, and Zhiyong Cheng. "Modeling Multi-Relational Connectivity for Personalized Fashion Matching." In MM '23: The 31st ACM International Conference on Multimedia. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3581783.3612583.

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