Time-Sensitive Language Modelling for Online Term Recurrence Prediction

Authors:
Dell Zhang;Jinsong Lu;Robert Mao;Jian-Yun Nie
Affiliations:
Birkbeck, University of London, London, UK WC1E 7HX;Birkbeck, University of London, London, UK WC1E 7HX;Microsoft Corp., Dublin, Ireland;University of Montreal, Canada H3C 3J7
Venue:
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
Year:
2009

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Abstract

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.