969 resultados para information filtering
Resumo:
A sound understanding of travellers’ behavioural changes and adaptation when facing a natural disaster is a key factor in efficiently and effectively managing transport networks at such times. This study specifically investigates the importance of travel/traffic information and its impact on travel behaviour during natural disasters. Using the 2011 Brisbane flood as a case study, survey respondents’ perceptions of the importance of travel/traffic information before, during, and after the flood were modelled using random-effects ordered logit. A hysteresis phenomenon was observed: respondents’ perceptions of the importance of travel/traffic information increased during the flood, and although its perceived importance decreased after the flood, it did not return to the pre-flood level. Results also reveal that socio-demographic features (such as gender and age) have a significant impact on respondents’ perceptions of the importance of travel/traffic information. The roles of travel time and safety in a respondent’s trip planning are also significantly correlated to their perception of the importance of this information. The analysis further shows that during the flood, respondents generally thought that travel/traffic information was important, and adjusted their travel plans according to information received. When controlling for other factors, the estimated odds of changing routes and cancelling trips for a respondent who thought that travel/traffic information was important, are respectively about three times and seven times the estimated odds for a respondent who thought that travel/traffic information was not important. In contrast, after the flood, the influence of travel/traffic information on respondents’ travel behaviour diminishes. Finally, the analysis shows no evidence of the influence of travel/traffic information’s on respondents’ travel mode; this indicates that inducing travel mode change is a challenging task.
Resumo:
User profiling is the process of constructing user models which represent personal characteristics and preferences of customers. User profiles play a central role in many recommender systems. Recommender systems recommend items to users based on user profiles, in which the items can be any objects which the users are interested in, such as documents, web pages, books, movies, etc. In recent years, multidimensional data are getting more and more attention for creating better recommender systems from both academia and industry. Additional metadata provides algorithms with more details for better understanding the interactions between users and items. However, most of the existing user/item profiling techniques for multidimensional data analyze data through splitting the multidimensional relations, which causes information loss of the multidimensionality. In this paper, we propose a user profiling approach using a tensor reduction algorithm, which we will show is based on a Tucker2 model. The proposed profiling approach incorporates latent interactions between all dimensions into user profiles, which significantly benefits the quality of neighborhood formation. We further propose to integrate the profiling approach into neighborhoodbased collaborative filtering recommender algorithms. Experimental results show significant improvements in terms of recommendation accuracy.
Resumo:
This reports a study that seeks to explore the experience of students majoring in technology and design in an undergraduate education degree. It examines their experiences in finding and using information for a practical assignment. In mapping the variation of the students' experience, the study uses a qualitative, interpretive approach to analyse the data, which was collected via one-to-one interviews. The analysis yielded five themes through which technology education students find and use information: interaction with others; experience (past and new); formal educational learning; the real world; and incidental occurrences. The intentions and strategies that form the students' approaches to finding and using information are discussed. So too are the implications for teaching practice.
Resumo:
New digital media surrounds us. Little is known, however, about the influence of technology devices such as tablets (e.g. iPads) and smart phones on young children’s lives in home and school settings, and what it means for them throughout their schooling and beyond. Most research to date has focused on children aged six years and older, and much less (with a few exceptions) on preschool-aged children. This article draws on parent interviews to show how family members engage with technology as part of the flow of everyday life. Only time and increased understandings of everyday practices will tell the real values and scope of using digital media.
Resumo:
Business Process Management describes a holistic management approach for the systematic design, modeling, execution, validation, monitoring and improvement of organizational business processes. Traditionally, most attention within this community has been given to control-flow aspects, i.e., the ordering and sequencing of business activities, oftentimes in isolation with regards to the context in which these activities occur. In this paper, we propose an approach that allows executable process models to be integrated with Geographic Information Systems. This approach enables process models to take geospatial and other geographic aspects into account in an explicit manner both during the modeling phase and the execution phase. We contribute a structured modeling methodology, based on the well-known Business Process Model and Notation standard, which is formalized by means of a mapping to executable Colored Petri nets. We illustrate the feasibility of our approach by means of a sustainability-focused case example of a process with important ecological concerns.
Resumo:
Focus groups are a popular qualitative research method for information systems researchers. However, compared with the abundance of research articles and handbooks on planning and conducting focus groups, surprisingly, there is little research on how to analyse focus group data. Moreover, those few articles that specifically address focus group analysis are all in fields other than information systems, and offer little specific guidance for information systems researchers. Further, even the studies that exist in other fields do not provide a systematic and integrated procedure to analyse both focus group ‘content’ and ‘interaction’ data. As the focus group is a valuable method to answer the research questions of many IS studies (in the business, government and society contexts), we believe that more attention should be paid to this method in the IS research. This paper offers a systematic and integrated procedure for qualitative focus group data analysis in information systems research.