Accurate and efficient query clustering via top ranked search results
Bibliographic record
Abstract
To make the search engine more user-friendly, commercial search engines commonly develop applications to provide suggestion or recommendation for every posed query. Clustering semantically similar queries acts as an essential prerequisite to function well in those applications. However, clustering queries effectively is quite challenging, since they are usually short, incomplete and ambiguous. Existing prevalent clustering methods, such as K-Means or DBSCAN cannot guarantee good performance in such a highly dimensional environment. Through analyzing users’ click-through query logs, hierarchical agglomerative clustering gives good results but is computationally quite expensive. This paper identifies a novel feature for clustering search queries based on a key insight – queries’ top ranked search results can themselves be used to quantify query similarity. After investigating such feature, we propose a new similarity metric for comparing those diverse queries. This facilitates us to develop two very efficient and accurate algorithms integrated in query clustering. We conduct comprehensive experiments to compare the accuracy of our approach against the known baselines along two dimensions: 1) quantifying the cohesion/separation of clustered queries, and 2) justifying the results by real-world Internet users. The experimental results demonstrate that our two algorithms and the similarity metric can generate more accurate results within a significantly shorter time.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".