Collaborative filtering

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  • The method formulates the collaborative filtering problem as classification problems and performs classification for all users simultaneously by using a modified boosting algorithm. This allows sharing common features among different classification tasks and thus reduces the negative effect of data sparseness. Experimental results show the effectiveness of the proposed method in comparison with other methods, especially when data are sparse.

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  • Hệ tư vấn (recommender system) đã trở thành một trong những lĩnh vực nghiên cứu quan trọng kể từ khi bài báo đầu tiên về lọc cộng tác (collaborative filtering) xuất hiện vào giữa những năm 1990. Hiện nay, sự quan tâm đối với hệ tư vấn đang rất cao vì sự cần thiết của những ứng dụng có thể giúp người dùng xử lý với tình trạng quá tải thông tin & đưa ra những nội dung hoặc lời khuyên phù hợp cho từng cá nhân. Một vài ứng dụng nổi tiếng như: hệ tư vấn sách, CDs của...

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  • Nowadays most companies are facing a great pressure caused by shorter product life cycle and wider product variety in the global market. To identify customer needs has become a crucial factor in any product development in this circumstance. It is an imperative to create a high-quality information channel that runs directly between customers in the target market and the product developers [24], who have to listen to the customer voice so that they can create customer centric values and thus meet the market competition.

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  • In 1996, the U.S. Department of Energy (DOE) and Mexico s Petr leos Mexicanos (PEMEX) began sponsoring this project to characterize the nature and sources of suspended particulate matter found in the ambient air in Mexico City. This collaborative effort follows another program sponsored by DOE and PEMEX in the early 1990 s to study gas phase pollutants and photochemical oxidants in the Valle de Mexico. 3,4 The high altitude, the year- round sunshine, and the complex atmospheric chemistry characteristic of Mexico City create a challenging laboratory for scientific research.

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  • part 1 book “teaching and learning in physical therapy – from classroom to clinic” has contents: filters - individual factors that influence us as teachers and learners; reflection and questions - developing self- awareness and critical thinking for continuous improvement in practice, communication and conflict negotiation - facilitating collaboration and empowering patients, family members, and peers,… and other contents.

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  • People use social media for a number of reasons: communicating, collaborating, seeking expert advice, sharing multimedia, presenting opinions, sharing reviews and for entertainment. Many claim that social media brings a new sense of community by allowing people to connect with others who are similar to themselves. While this might involve reconnecting with long lost friends from childhood or former coworkers, it is also used to meet new friends who have similar interests. An increasing number of people are using social media in their buying decisions.

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  • EURASIP Journal on Applied Signal Processing 2003:3, 312–316 c 2003 Hindawi Publishing Corporation Chebyshev Functions-Based New Designs of Halfband Low/Highpass Quasi-Equiripple FIR Digital Filters Ishtiaq Rasool Khan Department of Information and Media Sciences, The University of Kitakyushu, 1-1 Hibikino, Wakamatsu-ku, Kitakyushu 808-0135, Japan Collaboration Center, Kitakyushu Foundation for the Advancement of Industry, Science and Technology, 2-1 Hibikino, Wakamatsu-ku, Kitakyushu 808-0135, Japan Email: ir khan@hotmail.

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  • In order to answer each question the NCC-C information specialist developed a search strategy to identify relevant published evidence for both clinical and cost effectiveness. Key words and terms for the search were agreed in collaboration with the GDG. When required, the health economist searched for supplementary papers to inform detailed health economic work (see section on ‘Incorporating Health Economic Evidence’).

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