We address the problem of online term recurrence prediction: for a stream of terms, at each time point predict what term is going to recur next in the stream given the term occurrence history so far. It has many applications, for example, in Web search and social tagging. In this paper, we propose a time-sensitive language modelling approach to this problem that effectively combines term frequency and term recency information, and describe how this approach can be implemented efficiently by an online learning algorithm. Our experiments on a real-world Web query log dataset show significant improvements over standard language modelling. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Zhang, D., Lu, J., Mao, R., & Nie, J. Y. (2009). Time-sensitive language modelling for online term recurrence prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5766 LNCS, pp. 128–138). https://doi.org/10.1007/978-3-642-04417-5_12
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