It is a shared opinion that sustainable development requires a system discontinuity, meaning that radical changes in the way we produce and consume are needed. Within this framework there is an emerging understanding ...It is a shared opinion that sustainable development requires a system discontinuity, meaning that radical changes in the way we produce and consume are needed. Within this framework there is an emerging understanding that an important contribution to this change can be directly linked to decisions taken in the design phase of products, services and systems. Design schools have therefore to be able to provide design students with a broad knowledge and effective Design for Sustainability tools, in order to enable a new generation of designers in playing an active role in re-orienting our consumption and production patterns. This paper presents the intermediate results of the LeNS China, the Learning Network on Sustainability of Chinese design Higher Education Institutions aiming at curricula development on Design for Sustainability. The project is a regeneration of the LeNS Asian-European multi-polar network project financed by the European Commission. LeNS China is taking in consideration the local needs, interests and opportunities could represent a significant enabling platform capable to sensitise, support and empower a new generation of Chinese design educators, designers and entrepreneurs to reach design practice throughout an open collaborative learning approach. The paper will firstly introduce the LeNS project and its ethos, and then the LeNS China network will be described in terms of the state of the art of design for sustainability and its education in China, the scope and the objective, the results achieved so far and the next steps.展开更多
In view of the low interpretability of existing collaborative filtering recommendation algorithms and the difficulty of extracting information from content-based recommendation algorithms,we propose an efficient KGRS ...In view of the low interpretability of existing collaborative filtering recommendation algorithms and the difficulty of extracting information from content-based recommendation algorithms,we propose an efficient KGRS model.KGRS first obtains reasoning paths of knowledge graph and embeds the entities of paths into vectors based on knowledge representation learning TransD algorithm,then uses LSTM and soft attention mechanism to capture the semantic of each path reasoning,then uses convolution operation and pooling operation to distinguish the importance of different paths reasoning.Finally,through the full connection layer and sigmoid function to get the prediction ratings,and the items are sorted according to the prediction ratings to get the user’s recommendation list.KGRS is tested on the movielens-100k dataset.Compared with the related representative algorithm,including the state-of-the-art interpretable recommendation models RKGE and RippleNet,the experimental results show that KGRS has good recommendation interpretation and higher recommendation accuracy.展开更多
基金partially supported by National Social Science Fund of China (Grant No.11BH064)
文摘It is a shared opinion that sustainable development requires a system discontinuity, meaning that radical changes in the way we produce and consume are needed. Within this framework there is an emerging understanding that an important contribution to this change can be directly linked to decisions taken in the design phase of products, services and systems. Design schools have therefore to be able to provide design students with a broad knowledge and effective Design for Sustainability tools, in order to enable a new generation of designers in playing an active role in re-orienting our consumption and production patterns. This paper presents the intermediate results of the LeNS China, the Learning Network on Sustainability of Chinese design Higher Education Institutions aiming at curricula development on Design for Sustainability. The project is a regeneration of the LeNS Asian-European multi-polar network project financed by the European Commission. LeNS China is taking in consideration the local needs, interests and opportunities could represent a significant enabling platform capable to sensitise, support and empower a new generation of Chinese design educators, designers and entrepreneurs to reach design practice throughout an open collaborative learning approach. The paper will firstly introduce the LeNS project and its ethos, and then the LeNS China network will be described in terms of the state of the art of design for sustainability and its education in China, the scope and the objective, the results achieved so far and the next steps.
基金supported by the National Science Foundation of China Grant No.61762092“Dynamic multi-objective requirement optimization based on transfer learning”,No.61762089+2 种基金“The key research of high order tensor decomposition in distributed environment”the Open Foundation of the Key Laboratory in Software Engineering of Yunnan Province,Grant No.2017SE204,”Research on extracting software feature models using transfer learning”.
文摘In view of the low interpretability of existing collaborative filtering recommendation algorithms and the difficulty of extracting information from content-based recommendation algorithms,we propose an efficient KGRS model.KGRS first obtains reasoning paths of knowledge graph and embeds the entities of paths into vectors based on knowledge representation learning TransD algorithm,then uses LSTM and soft attention mechanism to capture the semantic of each path reasoning,then uses convolution operation and pooling operation to distinguish the importance of different paths reasoning.Finally,through the full connection layer and sigmoid function to get the prediction ratings,and the items are sorted according to the prediction ratings to get the user’s recommendation list.KGRS is tested on the movielens-100k dataset.Compared with the related representative algorithm,including the state-of-the-art interpretable recommendation models RKGE and RippleNet,the experimental results show that KGRS has good recommendation interpretation and higher recommendation accuracy.
