Feature-Based Question Routing in Community Question Answering Platforms
Bibliographic record
Abstract
<p>Community question answering (CQA) platforms became popular and indispensable sources of information in different domains. The success of these platforms relies heavily on the timely contribution of their expert users who would answer questions. There are many questions that remain unanswered for a long time, if not ever, on CQA platforms. In this dissertation, the problem of question routing on CQA platforms is addressed, which aims to connect experts to the right questions. We introduce and semantically classify 67 features and then train a learn to rank framework over five different CQA datasets. Results show that features based on tags, topics, user characteristics and user temporality are effective for question routing. Also the proposed approach outperforms the state-of-the-art neural matchmaking methods, that lack the interpretation of features, without compromising interpretability. The interpretation of features’ influence helps future research in this field to address the question routing problem more effectively.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".