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
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
VAST '07 Proceedings of the 2007 IEEE Symposium on Visual Analytics Science and Technology
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In this paper, we have developed an interactive approach to enable personalized news video recommendation. First, multi-modal information channels (audio, video and closed captions) are seamlessly integrated and synchronized to achieve more reliable news topic detection, and the contextual relationships between the news topics are extracted automatically. Second, topic network and hyperbolic visualization are seamlessly integrated to achieve interactive navigation and exploration of large-scale collections of news videos at the topic level, so that users can have a good global overview of large-scale collections of news videos at the first glance. In such interactive topic network navigation and exploration process, the user's personal background knowledge can be taken into consideration for obtaining the news topics of interest interactively, building up their mental models of news needs precisely and formulating their searches easily by selecting the visible news topics on the screen 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.