Temporal summaries of new topics
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Hourly analysis of a very large topically categorized web query log
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Exploring Independent Trends in a Topic-Based Search Engine
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Applying discrete PCA in data analysis
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Determining time of queries for re-ranking search results
ECDL'10 Proceedings of the 14th European conference on Research and advanced technology for digital libraries
Modeling geographic, temporal, and proximity contexts for improving geotemporal search
Journal of the American Society for Information Science and Technology
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In information retrieval relevance ranking of the results is one of the most important single tasks there are. There are many diffierent ranking algorithms based on the content of the documents or on some external properties e.g. link structure of html documents.We present a temporally adaptive content-based relevance ranking algorithm that explicitly takes into account the temporal behavior of the underlying statistical properties of the documents in the form of a statistical topic model. more we state that our algorithm can be used on top of any ranking algorithm.