Proceedings of the 2011 ACM Symposium on Applied Computing
Estimation methods for ranking recent information
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Supervised language modeling for temporal resolution of texts
Proceedings of the 20th ACM international conference on Information and knowledge management
Improving retrieval of short texts through document expansion
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
TASE: a time-aware search engine
Proceedings of the 21st ACM international conference on Information and knowledge management
Combining recency and topic-dependent temporal variation for microblog search
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Fast data in the era of big data: Twitter's real-time related query suggestion architecture
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
The Impacts of Structural Difference and Temporality of Tweets on Retrieval Effectiveness
ACM Transactions on Information Systems (TOIS)
How fresh do you want your search results?
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Improving pseudo-relevance feedback via tweet selection
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Exploiting temporal information in Web search
Expert Systems with Applications: An International Journal
Using temporal bursts for query modeling
Information Retrieval
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Time is an important dimension of relevance for a large number of searches, such as over blogs and news archives. So far, research on searching over such collections has largely focused on locating topically similar documents for a query. Unfortunately, topic similarity alone is not always sufficient for document ranking. In this paper, we observe that, for an important class of queries that we call time-sensitive queries, the publication time of the documents in a news archive is important and should be considered in conjunction with the topic similarity to derive the final document ranking. Earlier work has focused on improving retrieval for “recency” queries that target recent documents. We propose a more general framework for handling time-sensitive queries and we automatically identify the important time intervals that are likely to be of interest for a query. Then, we build scoring techniques that seamlessly integrate the temporal aspect into the overall ranking mechanism. We present an extensive experimental evaluation using a variety of news article data sets, including TREC data as well as real web data analyzed using the Amazon Mechanical Turk. We examine several techniques for detecting the important time intervals for a query over a news archive and for incorporating this information in the retrieval process. We show that our techniques are robust and significantly improve result quality for time-sensitive queries compared to state-of-the-art retrieval techniques.