Sustainable consumption: towards action and impact. : International scientific conference November 6th-8th 2011, Hamburg - European Green Capital 2011, Germany: abstract volume

This volume contains the abstracts of all oral and poster presentations of the international scientific conference „Sustainable Consumption – Towards Action and Impact“ held in Hamburg (Germany) on November 6th-8th 2011. This unique conference aims to promote a comprehensive academic discourse on issues concerning sustainable consumption and brings together scholars from a wide range of academic disciplines. In modern societies, private consumption is a multifaceted and ambivalent phenomenon: it is a ubiquitous social practice and an economic driving force, yet at the same time, its consequences are in conflict with important social and environmental sustainability goals. Finding paths towards “sustainable consumption” has therefore become a major political issue. In order to properly understand the challenge of “sustainable consumption”, identify unsustainable patterns of consumption and bring forward the necessary innovations, a collaborative effort of researchers from different disciplines is needed

Include 2011 : The role of inclusive design in making social innovation happen.

Include is the biennial conference held at the RCA and hosted by the Helen Hamlyn Centre for Design. The event is directed by Jo-Anne Bichard and attracts an international delegation

Re-mend: An accessible modular system for co-creation of customized clothing that caters to a wheelchair user’s personal style and fit.

“Clothing is a language we wear on our bodies, telling the world our story, our values and our sense of self” Wheelchair User. 11.1% of the United States population has mobility difficulties and the number is expected to rise in the coming years (CDC) yet they are unable to find clothing that addresses their style and needs. The study\u27s focus is on understanding the clothing needs and preferences of people with seated ability, an important step towards inclusivity in the fashion industry. People with seated ability face challenges in finding clothing that addresses both their functional and personal fashion language, hence highlighting the need for tailored and customizable solutions. The co-creation design approach, coupled with qualitative research methods, user journeys, and sharing pictures of ill-fitting clothing, proved to be a powerful and insightful process for designing a customizable clothing solution for people in wheelchairs. This led to Re-mend, a modular service model system for co-creation of customized clothing by leveraging community skills to make clothing functional, fashionable, and accessible. This approach has the potential to provide a more efficient and personalized service and to improve the quality of life of people in wheelchairs. Keywords : Adaptation, Wheelchair users, Customization, Clothing, measurements, co-creation model, modular system, style, fit, adaptive clothin

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System

Enhancing Fashion Sustainability Through a Data Systemic Approach

Today everyday life is characterized by the interaction with an ever-increasing flow of digital data. The research aims to analyze the fashion industry as a data-driven enterprise in which the correlation of data characterized by greater information power and higher quality gives the chance to make a more informed decision making that lead to undertaking better and more sustainable actions in all the value chain. Data, in this focus, could have the power of increasing the efficiency of the system and reducing its impact at the same time, creating a new model that is not only able to improve environmental, economic and social sustainability but also communicative, enabling a more human-centered products and services designing. This research highlights the importance of giving an integrated and holistic perspective through a data systemic approach to deal with a complex and fragmented sustainable problem, proposing an information flow strategy that makes accessible information improving transparency and traceability. This paper presents several case studies that show how data-oriented projects can contribute some benefits to a fashion system that has environmental sustainability as its priority, but also that the lack of correlation of all these strategies is not yet able to generate and lead to a systemic change

Interacting Attention-gated Recurrent Networks for Recommendation

Capturing the temporal dynamics of user preferences over items is important for recommendation. Existing methods mainly assume that all time steps in user-item interaction history are equally relevant to recommendation, which however does not apply in real-world scenarios where user-item interactions can often happen accidentally. More importantly, they learn user and item dynamics separately, thus failing to capture their joint effects on user-item interactions. To better model user and item dynamics, we present the Interacting Attention-gated Recurrent Network (IARN) which adopts the attention model to measure the relevance of each time step. In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping user-item interactions. By doing so, IARN can selectively memorize different time steps of a user's history when predicting her preferences over different items. Our model can therefore provide meaningful interpretations for recommendation results, which could be further enhanced by auxiliary features. Extensive validation on real-world datasets shows that IARN consistently outperforms state-of-the-art methods.Comment: Accepted by ACM International Conference on Information and Knowledge Management (CIKM), 201