Information seeking in electronic environments
Information seeking in electronic environments
TimeMine (demonstration session): visualizing automatically constructed timelines
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
ThemeRiver: Visualizing Thematic Changes in Large Document Collections
IEEE Transactions on Visualization and Computer Graphics
Visualizing the non-visual: spatial analysis and interaction with information from text documents
INFOVIS '95 Proceedings of the 1995 IEEE Symposium on Information Visualization
Personalizing search via automated analysis of interests and activities
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Towards content-based relevance ranking for video search
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Online video recommendation based on multimodal fusion and relevance feedback
Proceedings of the 6th ACM international conference on Image and video retrieval
Merging storyboard strategies and automatic retrieval for improving interactive video search
Proceedings of the 6th ACM international conference on Image and video retrieval
Semantic entity detection by integrating CRF and SVM
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Semantic entity-relationship model for large-scale multimedia news exploration and recommendation
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
SocialTransfer: cross-domain transfer learning from social streams for media applications
Proceedings of the 20th ACM international conference on Multimedia
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In this paper, a novel framework is developed to support personalized news video recommendation. First, multi-modal information sources for news videos are seamlessly integrated and synchronized to achieve more reliable news topic detection, and the contexts between different news topics are extracted automatically. Second, topic network and hyperbolic visualization are seamlessly integrated to support interactive navigation and exploration of large-scale collections of news videos at the topic level, so that users can gain deep insights of large-scale collections of news videos at the first glance. In such interactive topic network navigation and exploration process, users' personal background knowledge can be exploited for selecting news topics of interest interactively, building up their mental models of news needs precisely and formulating their queries easily by selecting the visible news topics on the topic network directly. Our system can further recommend the relevant web news, the new search directions, and the most relevant news videos according to their importance and representativeness scores. Our experiments on large-scale collections of news videos have provided very positive results.