Learning Fashion Compatibility with Bidirectional LSTMs

The ubiquity of online fashion shopping demands effective recommendation services for customers. In this paper, we study two types of fashion recommendation: (i) suggesting an item that matches existing components in a set to form a stylish outfit (a collection of fashion items), and (ii) generating an outfit with multimodal (images/text) specifications from a user. To this end, we propose to jointly learn a visual-semantic embedding and the compatibility relationships among fashion items in an end-to-end fashion. More specifically, we consider a fashion outfit to be a sequence (usually from top to bottom and then accessories) and each item in the outfit as a time step. Given the fashion items in an outfit, we train a bidirectional LSTM (Bi-LSTM) model to sequentially predict the next item conditioned on previous ones to learn their compatibility relationships. Further, we learn a visual-semantic space by regressing image features to their semantic representations aiming to inject attribute and category information as a regularization for training the LSTM. The trained network can not only perform the aforementioned recommendations effectively but also predict the compatibility of a given outfit. We conduct extensive experiments on our newly collected Polyvore dataset, and the results provide strong qualitative and quantitative evidence that our framework outperforms alternative methods.Comment: ACM MM 1

On the Design of Sales Support Systems for Online Apparel Stores

Many online stores apply several sales support systems, e.g., recommender systems, sorting and filtering tools, to support buyers during the shopping process. Although, the research highlights the positive effect of such systems, the current study questions its applicability in online stores for products which serve users\u27 needs to be unique like apparel or luxury products. We analyze female users\u27 buying behavior of apparel products in a laboratory setting and find that users with high trendiness undertake in general more search steps. Further, we find that most users rely during their search process on different sorting and filtering as well as on keyword search tools while personalized and non-personalized recommendations play a minor role for users in this industry. Further, we find that users with high trendiness avoid following top seller lists and wear with it -recommendations. Moreover, the provision of top seller rankings does not influence the consumers\u27 product choice

Spartan Daily, November 10, 1944

Volume 33, Issue 27https://scholarworks.sjsu.edu/spartandaily/10987/thumbnail.jp

Fashion-Specific Attributes Interpretation via Dual Gaussian Visual-Semantic Embedding

Several techniques to map various types of components, such as words, attributes, and images, into the embedded space have been studied. Most of them estimate the embedded representation of target entity as a point in the projective space. Some models, such as Word2Gauss, assume a probability distribution behind the embedded representation, which enables the spread or variance of the meaning of embedded target components to be captured and considered in more detail. We examine the method of estimating embedded representations as probability distributions for the interpretation of fashion-specific abstract and difficult-to-understand terms. Terms, such as "casual," "adult-casual,'' "beauty-casual," and "formal," are extremely subjective and abstract and are difficult for both experts and non-experts to understand, which discourages users from trying new fashion. We propose an end-to-end model called dual Gaussian visual-semantic embedding, which maps images and attributes in the same projective space and enables the interpretation of the meaning of these terms by its broad applications. We demonstrate the effectiveness of the proposed method through multifaceted experiments involving image and attribute mapping, image retrieval and re-ordering techniques, and a detailed theoretical/analytical discussion of the distance measure included in the loss function

Improving efficiency through layout optimization for Les Klar Couture.

Applied project submitted to the Department of Business Administration, Ashesi University, in partial fulfillment of Bachelor of Science degree in Business Administration, April 2019Les Klar Couture is a fashion company located in Ghana and noted for its simple, decent but classy designs. Selling at affordable prices, Les Klar hopes to expand its operations worldwide, a few years to come. It, however, focuses on empowering its clients to have confidence in themselves and to dress to suit their body sizes. The company currently runs its main branch at North Industrial Area and is yet to operate at its new branch in Kasoa. However, after conducting a needs assessment test, it was realized that the layout plan adopted in the old branch does not allow the workers to be very efficient due to lack of space. The owner is therefore unsure of which layout plan to adopt for the new branch. Thus, intensive research was carried out to explore and understand how the layout strategies adopted by firms, influences the efficiency of workers. Again, this can have a large influence on the perceptions clients create about a brand. A well designed layout also prevents casualties that may occur in the workplace. Thus, Les Klar adopted a layout plan that encompasses both the office layout and the process-oriented layout for the new branch. An implementation plan in a 3D format was created for the company to enable them to operate the new branch. Part of the solution was however implemented by the company but could not be fully implemented due to unavailability of the remaining equipment. For this solution to work effectively, the company should employ experts to aid with an inventory management system to help them minimize cost and to increase their presence on social media through various forms of advertisements.Ashesi Universit