CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Understanding temporal query dynamics
Proceedings of the fourth ACM international conference on Web search and data mining
A word at a time: computing word relatedness using temporal semantic analysis
Proceedings of the 20th international conference on World wide web
Keeping keywords fresh: a BM25 variation for personalized keyword extraction
Proceedings of the 2nd Temporal Web Analytics Workshop
Temporal pseudo-relevance feedback in microblog retrieval
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Time-sensitive query auto-completion
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Expediting search trend detection via prediction of query counts
Proceedings of the sixth ACM international conference on Web search and data mining
Fast candidate generation for real-time tweet search with bloom filter chains
ACM Transactions on Information Systems (TOIS)
Behavioral dynamics on the web: Learning, modeling, and prediction
ACM Transactions on Information Systems (TOIS)
Information Retrieval with Time Series Query
Proceedings of the 2013 Conference on the Theory of Information Retrieval
Using temporal bursts for query modeling
Information Retrieval
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Common measures of term importance in information retrieval (IR) rely on counts of term frequency; rare terms receive higher weight in document ranking than common terms receive. However, realistic scenarios yield additional information about terms in a collection. Of interest in this article is the temporal behavior of terms as a collection changes over time. We propose capturing each term's collection frequency at discrete time intervals over the lifespan of a corpus and analyzing the resulting time series. We hypothesize the collection frequency of a weakly discriminative term x at time t is predictable by a linear model of the term's prior observations. On the other hand, a linear time series model for a strong discriminators' collection frequency will yield a poor fit to the data. Operationalizing this hypothesis, we induce three time-based measures of term importance and test these against state-of-the-art term weighting models. © 2010 Wiley Periodicals, Inc.