The 21 st century is a time of digitization and infurmationization. Online media have been going forward rapidly and have penetrated into various aspects of people＇ s life, thus having a significant impact on people...The 21 st century is a time of digitization and infurmationization. Online media have been going forward rapidly and have penetrated into various aspects of people＇ s life, thus having a significant impact on people＇ s production and life. Under such circumstance, traditional fashion indnstry has also been influenced by online media and has gradually transformed to digitalization. Fashion design is likely to get away from simplex manual design and to transform to the design method of man-machine synergy. The introduction of network technique into fashion design will bring vast potential for future development of fashion design.展开更多
Nowadays, software engineers use a variety of online media to search and become informed of new and interesting technologies, and to learn from and help one another. We refer to these kinds of online media which help ...Nowadays, software engineers use a variety of online media to search and become informed of new and interesting technologies, and to learn from and help one another. We refer to these kinds of online media which help software engineers improve their performance in software development, maintenance, and test processes as software information sites. In this paper, we propose TagCombine, an automatic tag recommendation method which analyzes objects in software information sites. TagCombine has three different components： 1） multi-label ranking component which considers tag recommendation as a multi-label learning problem; 2） similarity-based ranking component which recommends tags from similar objects; 3） tag-term based ranking component which considers the relationship between different terms and tags, and recommends tags after analyzing the terms in the objects. We evaluate TagCombine on four software information sites, Ask Different, Ask Ubuntu, Feecode, and Stack Overflow. On averaging across the four projects, TagCombine achieves recall@5 and recallS10 to 0.619 8 and 0.762 5 respectively, which improves TagRec proposed by Al-Kofahi et al. by 14.56% and 10.55% respectively, and the tag recommendation method proposed by Zangerle et al. by 12.08% and 8.16% respectively.展开更多
文摘The 21 st century is a time of digitization and infurmationization. Online media have been going forward rapidly and have penetrated into various aspects of people＇ s life, thus having a significant impact on people＇ s production and life. Under such circumstance, traditional fashion indnstry has also been influenced by online media and has gradually transformed to digitalization. Fashion design is likely to get away from simplex manual design and to transform to the design method of man-machine synergy. The introduction of network technique into fashion design will bring vast potential for future development of fashion design.
文摘Nowadays, software engineers use a variety of online media to search and become informed of new and interesting technologies, and to learn from and help one another. We refer to these kinds of online media which help software engineers improve their performance in software development, maintenance, and test processes as software information sites. In this paper, we propose TagCombine, an automatic tag recommendation method which analyzes objects in software information sites. TagCombine has three different components： 1） multi-label ranking component which considers tag recommendation as a multi-label learning problem; 2） similarity-based ranking component which recommends tags from similar objects; 3） tag-term based ranking component which considers the relationship between different terms and tags, and recommends tags after analyzing the terms in the objects. We evaluate TagCombine on four software information sites, Ask Different, Ask Ubuntu, Feecode, and Stack Overflow. On averaging across the four projects, TagCombine achieves recall@5 and recallS10 to 0.619 8 and 0.762 5 respectively, which improves TagRec proposed by Al-Kofahi et al. by 14.56% and 10.55% respectively, and the tag recommendation method proposed by Zangerle et al. by 12.08% and 8.16% respectively.
