Computational Technologies for Fashion Recommendation: A Survey

Fashion recommendation is a key research field in computational fashion research and has attracted considerable interest in the computer vision, multimedia, and information retrieval communities in recent years. Due to the great demand for applications, various fashion recommendation tasks, such as personalized fashion product recommendation, complementary (mix-and-match) recommendation, and outfit recommendation, have been posed and explored in the literature. The continuing research attention and advances impel us to look back and in-depth into the field for a better understanding. In this paper, we comprehensively review recent research efforts on fashion recommendation from a technological perspective. We first introduce fashion recommendation at a macro level and analyse its characteristics and differences with general recommendation tasks. We then clearly categorize different fashion recommendation efforts into several sub-tasks and focus on each sub-task in terms of its problem formulation, research focus, state-of-the-art methods, and limitations. We also summarize the datasets proposed in the literature for use in fashion recommendation studies to give readers a brief illustration. Finally, we discuss several promising directions for future research in this field. Overall, this survey systematically reviews the development of fashion recommendation research. It also discusses the current limitations and gaps between academic research and the real needs of the fashion industry. In the process, we offer a deep insight into how the fashion industry could benefit from fashion recommendation technologies. the computational technologies of fashion recommendation

ICT-based solution approach for collaborative delivery of customised products

© 2016 Taylor & Francis. Growing interest on business collaboration motivates todays industries, especially small and medium enterprises (SMEs) to establish and manage dynamic and non-hierarchical business networks to respond to market opportunities with added business benefits. This business environment requires concurrent work and real-time information sharing between key business partners in order to design and develop customised products. The use of traditional communication media such as e-mail, phone and fax is not satisfactory to get real-time information and is time-consuming and most often ineffective. In such environments, an Information and Communication Technology (ICT)/Web-based technology supports real-time information sharing among networked SMEs for the collaborative design and manufacturing of customised products. This study proposes an innovative ICT platform supporting SMEs collaboration, through Web and the Internet of Things technologies, which follows the Net-Challenge conceptual approach and methodological guidelines for SMEs to form and operate virtual organisations for the collaborative development and delivery of customised products. The ICT Platform was assessed in three different industry domains, namely the textile and apparel, the footwear and the machine tools, respectively. This ICT solution promotes collaboration, with substantial advantages for SMEs including the reduction of costs and delivery time and improvement of the innovation potential

Deep Learning for Online Fashion: A Novel Solution for the Retail E-Commerce Industry

The online shopping experience for clothing can be further enhanced by implementing Deep Learning techniques, such as Computer Vision and personalized recommendation systems. Automation, as a principle, can be applied to solving problems surrounding efficacy, efficiency, and security. It also provides a layer of abstraction for the user during the online shopping experience. This research aims to apply Deep Learning methods and principles of automation to augment the e-commerce fashion market in a novel way. After using these methods, it was found that Convolutional Autoencoders and Item-to-Item Based Recommenders may be used to accurately and precisely recommend articles of clothing based on a users’ styling preferences

The FASHION Visual Search using Deep Learning Approach

In recent years, the World Wide Web (WWW) has established itself as a popular source of information. Using an effective approach to investigate the vast amount of information available on the internet is essential if we are to make the most of the resources available. Visual data cannot be indexed using text-based indexing algorithms because it is significantly larger and more complex than text. Content-Based Image Retrieval, as a result, has gained widespread attention among the scientific community (CBIR). Input into a CBIR system that is dependent on visible features of the user\u27s input image at a low level is difficult for the user to formulate, especially when the system is reliant on visible features at a low level because it is difficult for the user to formulate. In addition, the system does not produce adequate results. To improve task performance, the CBIR system heavily relies on research into effective feature representations and appropriate similarity measures, both of which are currently being conducted. In particular, the semantic chasm that exists between low-level pixels in images and high-level semantics as interpreted by humans has been identified as the root cause of the issue. There are two potentially difficult issues that the e-commerce industry is currently dealing with, and the study at hand addresses them. First, handling manual labeling of products as well as second uploading product photographs to the platform for sale are two issues that merchants must contend with. Consequently, it does not appear in the search results as a result of misclassifications. Moreover, customers who don\u27t know the exact keywords but only have a general idea of what they want to buy may encounter a bottleneck when placing their orders. By allowing buyers to click on a picture of an object and search for related products without having to type anything in, an image-based search algorithm has the potential to unlock the full potential of e-commerce and allow it to reach its full potential. Inspired by the current success of deep learning methods for computer vision applications, we set out to test a cutting-edge deep learning method known as the Convolutional Neural Network (CNN) for investigating feature representations and similarity measures. We were motivated to do so by the current success of deep learning methods for computer vision applications (CV). According to the experimental results presented in this study, a deep machine learning approach can be used to address these issues effectively. In this study, a proposed Deep Fashion Convolution Neural Network (DFCNN) model that takes advantage of transfer learning features is used to classify fashion products and predict their performance. The experimental results for image-based search reveal improved performance for the performance parameters that were evaluated

SizeNet: Weakly Supervised Learning of Visual Size and Fit in Fashion Images

Finding clothes that fit is a hot topic in the e-commerce fashion industry. Most approaches addressing this problem are based on statistical methods relying on historical data of articles purchased and returned to the store. Such approaches suffer from the cold start problem for the thousands of articles appearing on the shopping platforms every day, for which no prior purchase history is available. We propose to employ visual data to infer size and fit characteristics of fashion articles. We introduce SizeNet, a weakly-supervised teacher-student training framework that leverages the power of statistical models combined with the rich visual information from article images to learn visual cues for size and fit characteristics, capable of tackling the challenging cold start problem. Detailed experiments are performed on thousands of textile garments, including dresses, trousers, knitwear, tops, etc. from hundreds of different brands.Comment: IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW) 2019 Focus on Fashion and Subjective Search - Understanding Subjective Attributes of Data (FFSS-USAD

An aesthetic for sustainable interactions in product-service systems?

Copyright @ 2012 Greenleaf PublishingEco-efficient Product-Service System (PSS) innovations represent a promising approach to sustainability. However the application of this concept is still very limited because its implementation and diffusion is hindered by several barriers (cultural, corporate and regulative ones). The paper investigates the barriers that affect the attractiveness and acceptation of eco-efficient PSS alternatives, and opens the debate on the aesthetic of eco-efficient PSS, and the way in which aesthetic could enhance some specific inner qualities of this kinds of innovations. Integrating insights from semiotics, the paper outlines some first research hypothesis on how the aesthetic elements of an eco-efficient PSS could facilitate user attraction, acceptation and satisfaction