A vlHMM approach to context-aware search
Bibliographic record
Abstract
Capturing the context of a user's query from the previous queries and clicks in the same session leads to a better understanding of the user's information need. A context-aware approach to document reranking, URL recommendation, and query suggestion may substantially improve users' search experience. In this article, we propose a general approach to context-aware search by learning a variable length hidden Markov model ( vlHMM ) from search sessions extracted from log data. While the mathematical model is powerful, the huge amounts of log data present great challenges. We develop several distributed learning techniques to learn a very large vlHMM under the map-reduce framework. Moreover, we construct feature vectors for each state of the vlHMM model to handle users' novel queries not covered by the training data. We test our approach on a raw dataset consisting of 1.9 billion queries, 2.9 billion clicks, and 1.2 billion search sessions before filtering, and evaluate the effectiveness of the vlHMM learned from the real data on three search applications: document reranking, query suggestion, and URL recommendation. The experiment results validate the effectiveness of vlHMM in the applications of document reranking, URL recommendation, and query suggestion.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".