Feedback Ranking Method in Topic-Based Retrieval
Bibliographic record
Abstract
Ranking has an extensive application in analyzing public opinions of social network (SN), such as searching the most hot topic or the most relevant articles that the user concerning. In these scenarios, due to the different requirements of users, there is need to rank the object set from different aspects and to re-rank the object set by integrating these different results to acquire a synthesize rank result.In this paper, we proposed a novel Feedback Ranking method, which lets two basic rankers learn from each other during the mutual process by providing each one's result as feedback to the other so as to boost the ranking performance. During the mutual ranking refinement process, we utilize iSRCC---an improvement on Spearman Rank Correlation to calculate the weight of each basic rankers dynamically. We apply this method into the article ranking problem on topic-query retrieval and evaluate its effectiveness on the TAC09 data set. Overall evaluation results are promising.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".