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Kinh te quoc te
Kinh te quoc te
hiennho811
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Presented at The Third ASE Social Informatics conference at Harvard University. Abstract: Web applications exploit user information from social networks and online user activities to facilitate interaction and create an enhanced user experience. Due to privacy issues however, it might be difficult to extract user data from social network, in particular location data. For instance, information on user location depends on users’ agreement to share own geographic data. Instead of directly collecting personal user information, we aim to infer user preferences by detecting behavior patterns from publicly available micro blogging content and users’ followers’ network. With statistical and machine-learning methods, we employ Twitter-specific features to predict country origin of users on Twitter with an accuracy of more than 90% for users from the most active countries. We further investigate users’ interpersonal communication with their followers. Our findings reveal that belonging to a particular cultural group is playing an important role in increasing users responses to their friends. The knowledge on user cultural origins thus could provide a differentiated state-of-the-art user experience in microblogs, for instance, in friend recommendation scenario.
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งานวันปิยมหาราช ประจำปี 2556
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This is my article on culture oriented user modeling presented at the ASE International Conference on Social Informatics in Washington D.C. US on 14 December 2012. This work received the Best Paper Award.
A User Modeling Oriented Analysis of Cultural Backgrounds in Microblogging
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Kinh te quoc te
Kinh te quoc te
hiennho811
Laporan kdv akmal
Laporan kdv akmal
Akmal_sidiq
Presented at The Third ASE Social Informatics conference at Harvard University. Abstract: Web applications exploit user information from social networks and online user activities to facilitate interaction and create an enhanced user experience. Due to privacy issues however, it might be difficult to extract user data from social network, in particular location data. For instance, information on user location depends on users’ agreement to share own geographic data. Instead of directly collecting personal user information, we aim to infer user preferences by detecting behavior patterns from publicly available micro blogging content and users’ followers’ network. With statistical and machine-learning methods, we employ Twitter-specific features to predict country origin of users on Twitter with an accuracy of more than 90% for users from the most active countries. We further investigate users’ interpersonal communication with their followers. Our findings reveal that belonging to a particular cultural group is playing an important role in increasing users responses to their friends. The knowledge on user cultural origins thus could provide a differentiated state-of-the-art user experience in microblogs, for instance, in friend recommendation scenario.
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Efffective presentation - Dr Sunil Kardekar
Efffective presentation - Dr Sunil Kardekar
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My presentation @ MAPCON Regional pediatrics conference 15th & 16th Dec 2012 Aurangabad
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Dwm Multimedia
This is my article on culture oriented user modeling presented at the ASE International Conference on Social Informatics in Washington D.C. US on 14 December 2012. This work received the Best Paper Award.
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AWSでAPI Gatewayから非同期でLambdaを起動してS3にファイルアップロードしようとしたらハマった話。
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