@inproceedings{b173e25edda248ff89945fb127803052,
title = "Estimation methods for ranking recent information",
abstract = "Temporal aspects of documents can impact relevance for certain kinds of queries. In this paper, we build on earlier work of modeling temporal information. We propose an extension to the Query Likelihood Model that incorporates query-specific information to estimate rate parameters, and we introduce a temporal factor into language model smoothing and query expansion using pseudo-relevance feedback. We evaluate these extensions using a Twitter corpus and two newspaper article collections. Results suggest that, compared to prior approaches, our models are more effective at capturing the temporal variability of relevance associated with some topics.",
keywords = "Information retrieval, Microblogs, Ranking algorithms, Time",
author = "Miles Efron and Gene Golovchinsky",
year = "2011",
doi = "10.1145/2009916.2009984",
language = "English (US)",
isbn = "9781450309349",
series = "SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval",
publisher = "Association for Computing Machinery",
pages = "495--504",
booktitle = "SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval",
address = "United States",
note = "34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011 ; Conference date: 24-07-2011 Through 28-07-2011",
}