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. 2008 Sep 12:12:487-503.
doi: 10.1007/s10791-008-9067-7.

Modeling Actions of PubMed Users with N-Gram Language Models

Jimmy Lin  1 , W John Wilbur
Affiliations

Modeling Actions of PubMed Users with N-Gram Language Models

Jimmy Lin et al. Inf Retr Boston. .

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((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.

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Figures

Figure 1
Figure 1
Characteristics of episodes generated by applying different thresholds to segment sessions. Values above each filled square indicate the percentage of singleton episodes that consists of a single retrieve action.
Figure 2
Figure 2
Distribution of episode length, in terms of number of transactions (top) and duration (bottom). Duration is binned in 5 minute intervals (e.g., ‘5’ represents intervals between 20–25 minutes).
Figure 3
Figure 3
Perplexity of session and episode test data on different n-gram language models.
Figure 4
Figure 4
Accuracy of predicting next user action using different n-gram language models: session data on top, episode data on bottom. Solid line in each graph indicates baseline (most frequent class).
Figure 5
Figure 5
Relative likelihood of observing a particular action after a consecutive sequence of the same action. For example, the probability of ‘L’ followed by another ‘L’ is 12 times higher than expected by chance.
See this image and copyright information in PMC

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