@article {15293,
	title = {Modeling actions of PubMed users with n-gram language models},
	journal = {Information retrieval},
	volume = {12},
	year = {2009},
	month = {2009///},
	pages = {487 - 503},
	abstract = {Transaction logs from online search engines are valuable for two reasons: First, they provide insight into human information-seeking behavior. Second, log data can be used to train user models, which can then be applied to improve retrieval systems. This article presents a study of logs from PubMed{\textregistered}, the public gateway to the MEDLINE{\textregistered} database of bibliographic records from the medical and biomedical primary literature. Unlike most previous studies on general Web search, our work examines user activities with a highly-specialized search engine. We encode user actions as string sequences and model these sequences using n-gram language models. The models are evaluated in terms of perplexity and in a sequence prediction task. They help us better understand how PubMed users search for information and provide an enabler for improving users{\textquoteright} search experience.},
	doi = {10.1007/s10791-008-9067-7},
	author = {Jimmy Lin and Wilbur,W. J}
}
