CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Using temporal profiles of queries for precision prediction
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Temporal document retrieval model for business news archives
Information Processing and Management: an International Journal - Special issue: Cross-language information retrieval
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
A language modeling approach for temporal information needs
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
TASE: a time-aware search engine
Proceedings of the 21st ACM international conference on Information and knowledge management
Exploiting temporal information in Web search
Expert Systems with Applications: An International Journal
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When searching a temporal document collection, e.g., news archives or blogs, the time dimension must be explicitly incorporated into a retrieval model in order to improve relevance ranking. Previous work has followed one of two main approaches: 1) a mixture model linearly combining textual similarity and temporal similarity, or 2) a probabilistic model generating a query from the textual and temporal part of a document independently. In this paper, we compare the effectiveness of different time-aware ranking methods by using a mixture model applied to all methods. Extensive evaluation is conducted using the New York Times Annotated Corpus, queries and relevance judgments obtained using the Amazon Mechanical Turk.