Inproceedings,

A comparison of time-aware ranking methods

, and .
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, page 1257--1258. New York, NY, USA, ACM, (2011)
DOI: 10.1145/2009916.2010147

Abstract

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.

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