High-end fashion manufacturing in the UK - product, process and vision. Recommendations for education, training and accreditation

The Centre for Fashion Enterprise (CFE) was commissioned by the Department of Culture, Media and Sport (DCMS) to undertake a feasibility study to explore fully the market need for a new high-end production hub. This was in direct response to the need highlighted in the DCMS report, Creative Britain - New Talents For The New Economy, published in 2008. In addition to finding a need for a sampling and innovation facility1 (outlined in a separate document), the study identified significant problems relating to education and skills training in the sector. Recommendations are given in this report as to how these might be addressed, as well as a recommendation for an accreditation scheme that would aim to raise production quality standards within the sector

High-end fashion manufacturing in the UK - product, process and vision: Recommendations for a Designer and Fashion Manufacturer Alliance and a Designer Innovation and Sampling Centre

The Centre for Fashion Enterprise (CFE) was commissioned by the Department of Culture, Media and Sport (DCMS) to undertake a feasibility study to explore fully the market need for a new high-end production hub. This was in direct response to the need highlighted in the DCMS report, Creative Britain - New Talents For The New Economy, published in 2008. This study has confirmed that there is a need. However the need is for a sampling and innovation facility rather than a production hub. Designers reported a shortage of high quality sampling capacity in the UK, as well as difficulties in getting small quantities produced. Additionally, they do not know where or how to source appropriate manufacturing in the UK, Europe or globally, at the quality the market requires

Who are Like-minded: Mining User Interest Similarity in Online Social Networks

In this paper, we mine and learn to predict how similar a pair of users' interests towards videos are, based on demographic (age, gender and location) and social (friendship, interaction and group membership) information of these users. We use the video access patterns of active users as ground truth (a form of benchmark). We adopt tag-based user profiling to establish this ground truth, and justify why it is used instead of video-based methods, or many latent topic models such as LDA and Collaborative Filtering approaches. We then show the effectiveness of the different demographic and social features, and their combinations and derivatives, in predicting user interest similarity, based on different machine-learning methods for combining multiple features. We propose a hybrid tree-encoded linear model for combining the features, and show that it out-performs other linear and treebased models. Our methods can be used to predict user interest similarity when the ground-truth is not available, e.g. for new users, or inactive users whose interests may have changed from old access data, and is useful for video recommendation. Our study is based on a rich dataset from Tencent, a popular service provider of social networks, video services, and various other services in China

Report on the Information Retrieval Festival (IRFest2017)

The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017

Content and Context: Identifying the Impact of Qualitative Information on Consumer Choice

Managers and researchers alike suspect that the vast amounts of qualitative information in blogs, reviews, news stories, and experts’ advice influence consumer behavior. But, does qualitative information impact or rather reflect consumer choices? We argue that because message content and consumer choice are endogenous, non-random selection and conflation of awareness and persuasion complicate causal estimation of the impact of message content on outcomes. We apply Latent Dirichlet Allocation to characterize the topics of transcribed content from 2,397 stock recommendations provided by Jim Cramer on his show Mad Money. We demonstrate that selection bias and audience prior awareness create measurable biases in estimates of the impact of content on stock prices. Comparing recommendation content to prior news, we show that he is less persuasive when he uses more novel arguments. The technique we develop can be applied in a variety of settings where marketers can present different messages depending on what subjects know

Do President Trump's tweets affect financial markets?

Frequent tweets of the former president of the United States, Donald Trump, provide a unique opportunity to study how financial markets respond to his statements. To do this, we utilize a precise timestamp of each tweet together with high-frequency financial data. We start by analyzing the impact of tweets in general, irrespective of their content. We find that tweets by President Trump are followed by increased uncertainty, increased trading and a decline in the US stock market. We utilize two methods in order to study whether the market reaction depends on the content of the tweets. First, classification of Trump's tweets depending on whether they contain a specific word reveals that market response is particularly negative for tweets containing the words “products” and “tariff”. Second, we use Latent Dirichlet Allocation to affiliate tweets with distinct topics. We find that while most topics do not impact financial markets, the US stock market responds to tweets related to the topic of a “trade war” by price decline, increased trading volume and increased uncertainty. The “trade war” tweets affect other financial markets too, as the Chinese stock market responds to these tweets negatively, while the price of gold responds positively. We illustrate the practical importance of our approach by an automated trading system, which achieves positive abnormal returns.publishedVersio

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace