Clustering Users by Information-Seeking Style: An Empirical Study on an Academic Search Engine
Bibliographic record
Abstract
Nowadays, academic search engines have become indispensable tools for getting important online scholarly information. User differences are important factors that influence the use of information systems. The way people use academic search engines to find information varies depending on their information-seeking style. Therefore, finding and understanding different information-seeking behaviors has become an important line of research. User behavior patterns can be discovered by examining user interaction logs to determine who the users are and what they intend to do. These insights can be useful in designing more optimized academic search engines. In this paper, we analyze the user interaction logs collected from the Iranian scientific information database. The Ganj database is the official repository for collecting and organizing theses and dissertations in Iran. Many researchers search scientific and research resources from the Ganj database daily. We use a sequential pattern mining approach to extract frequent sequential behavior patterns on user interaction logs and to cluster users into three groups based on their frequent behavior patterns, using the K-means clustering algorithm. Cluster analysis shows that users with similar frequent behavior patterns have similar information-seeking styles. Finally, we found three clusters and named them: fast surfers, deep divers, and broad scanners. Our findings can help developers of academic search engines and policymakers to identify users' needs and priorities to make better decisions to design a reasonable page layout and well-organized website for all users based on their search styles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".