Modeling Actions of PubMed Users with N-Gram Language Models.
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((R)), the public gateway to the MEDLINE((R)) 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' search experience.
Lin
Jimmy
J
The iSchool, College of Information Studies, University of Maryland, College Park, Maryland, USA, jimmylin@umd.edu.
Wilbur
W John
WJ
J Am Med Inform Assoc. 2007 Mar-Apr;14(2):212-20
17213501
BMC Bioinformatics. 2007 Oct 30;8:423
17971238
J Med Libr Assoc. 2003 Apr;91(2):231-40
12883574
JAMA. 1983 Nov 11;250(18):2494-9
6355532
Med Ref Serv Q. 1989 Winter;8(4):61-79
10304249
J Am Med Inform Assoc. 1994 Nov-Dec;1(6):447-58
7850570