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

Semi-supervised Adversarial Learning for Complementary Item Recommendation

Complementary item recommendations are a ubiquitous feature of modern e-commerce sites. Such recommendations are highly effective when they are based on collaborative signals like co-purchase statistics. In certain online marketplaces, however, e.g., on online auction sites, constantly new items are added to the catalog. In such cases, complementary item recommendations are often based on item side-information due to a lack of interaction data. In this work, we propose a novel approach that can leverage both item side-information and labeled complementary item pairs to generate effective complementary recommendations for cold items, i.e., for items for which no co-purchase statistics yet exist. Given that complementary items typically have to be of a different category than the seed item, we technically maintain a latent space for each item category. Simultaneously, we learn to project distributed item representations into these category spaces to determine suitable recommendations. The main learning process in our architecture utilizes labeled pairs of complementary items. In addition, we adopt ideas from Cycle Generative Adversarial Networks (CycleGAN) to leverage available item information even in case no labeled data exists for a given item and category. Experiments on three e-commerce datasets show that our method is highly effective.Comment: ACM Web Conference 202

Scenario pedagogy as a negotiated, multimodal approach to developing professional communication practices in higher education

Includes bibliographical references.The focus of this study is pedagogy - the 'how' of teaching. In particular, a negotiated and multimodal pedagogical approach which I have coined scenario pedagogy is of interest. Scenario pedagogy involves embedding an entire curriculum into a topical and authentic scenario, relevant to a particular group of students in higher education. The course in question is professional communication and the target group comprises senior and post-graduate accounting and other finance and information systems students registered in the commerce faculty. They are not communication students per se but register for a one-semester professional communication course towards their respective commerce degrees. In this study I examine how these students develop their professional communication practices using a wide variety of verbal and visual semiotic resources. Their selection of hybrid discursive, generic and modal resources are foregrounded at both draft and final product stage and include their communicative processes as well as the material artefacts they deliver in class. How students instantiate their meaning making and emerging identity as professionals-to-be is highlighted against a pedagogical framework of negotiated design. This framework combines a multiliteracies cum multimodal perspective which is underpinned by the notion of transformed practice. As pivotal elements of transformation - personally, collectively and societally - education and communication play significant roles, particularly in post-Apartheid South Africa still characterised by enormous socio-economic disparities and disadvantage

Foundations and Recent Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design computer agents with intelligent capabilities such as understanding, reasoning, and learning through integrating multiple communicative modalities, including linguistic, acoustic, visual, tactile, and physiological messages. With the recent interest in video understanding, embodied autonomous agents, text-to-image generation, and multisensor fusion in application domains such as healthcare and robotics, multimodal machine learning has brought unique computational and theoretical challenges to the machine learning community given the heterogeneity of data sources and the interconnections often found between modalities. However, the breadth of progress in multimodal research has made it difficult to identify the common themes and open questions in the field. By synthesizing a broad range of application domains and theoretical frameworks from both historical and recent perspectives, this paper is designed to provide an overview of the computational and theoretical foundations of multimodal machine learning. We start by defining two key principles of modality heterogeneity and interconnections that have driven subsequent innovations, and propose a taxonomy of 6 core technical challenges: representation, alignment, reasoning, generation, transference, and quantification covering historical and recent trends. Recent technical achievements will be presented through the lens of this taxonomy, allowing researchers to understand the similarities and differences across new approaches. We end by motivating several open problems for future research as identified by our taxonomy

Analyzing Impact of Aesthetic Visual Design on Usability of E-Learning: An Emerging Economy Perspective

Objectives: The purpose of the study is to examine various dimensions of aesthetic visual design and their role in predicting usability in e-learning in higher education institutions of northern India. Using quantitative means of data collection, this research identified, ways and means to make learning content effectively usable, that is, attractive, interesting, motivating, and engaging for the learners. Method: A survey questionnaire was developed through focused group discussions with field experts. Data were collected through online as well as offline modes. A Google form was created and its weblink was shared with the students pursuing degree courses in various state universities in northern India. Several visits and revisits were also undertaken to various universities to approach the respondents Results: Results confirmed consistency, typography, graphics, grid, and layout as factors responsible for predicting usability of e-learning. Surprisingly, color and compositional guidelines emerged insignificant. Implications: The study has implications for teaching and learning activities that promote effective learning. The findings are beneficial for course-design faculty who develop modules by considering visual design elements that can facilitate interaction with and understanding of content by students learning in an online modality