Add V3 eval metrics: nDCG@10=0.9494, hard_neg_auc=0.7380
Browse files
production_model/eval_metrics_v3.json
ADDED
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@@ -0,0 +1,91 @@
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{
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"model": "LightGBM LambdaRank V3",
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"description": "Trained on cross-survey authority labels with 13 graph-based features",
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"num_features": 13,
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"features": [
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"candidate_num_cited_by",
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"candidate_num_references",
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"survey_num_references",
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"co_citation_count",
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"bibliographic_coupling",
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"jaccard_refs",
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"adamic_adar",
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"n_surveys_citing",
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"n_total_citations",
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"cited_by_survey_refs",
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"candidate_cites_survey_refs",
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"shared_citers_with_survey",
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"hub_score"
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],
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"training": {
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"train_rows": 58749,
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"train_queries": 486,
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"eval_rows": 1825,
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"eval_queries": 14,
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"best_iteration": 1,
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"training_time_seconds": 0.4
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},
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"eval_metrics": {
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"ndcg@10": 0.9515,
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"ndcg@20": 0.9607,
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"mrr": 0.9524,
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"hard_neg_auc": 0.7364
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},
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"train_metrics": {
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"ndcg@10": 0.9494,
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"hard_neg_auc": 0.738
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},
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"comparison": {
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"Random": {
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"ndcg@10": 0.2356,
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"hard_neg_auc": 0.5039
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},
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"CitationCount": {
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"ndcg@10": 0.8371,
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"hard_neg_auc": 0.6277
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},
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"Authority": {
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"ndcg@10": 0.929,
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"hard_neg_auc": 0.6463
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},
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"V3_LightGBM": {
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"ndcg@10": 0.9494,
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"hard_neg_auc": 0.738
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},
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"Oracle": {
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"ndcg@10": 1.0,
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"hard_neg_auc": 1.0
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}
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},
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"feature_importance": [
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{
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"feature": "n_surveys_citing",
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"importance": 1607.3
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},
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{
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"feature": "hub_score",
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"importance": 39.8
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},
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{
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"feature": "survey_num_references",
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"importance": 33.0
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},
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{
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"feature": "adamic_adar",
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"importance": 5.8
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},
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{
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"feature": "candidate_num_references",
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"importance": 3.3
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},
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{
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"feature": "jaccard_refs",
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"importance": 2.7
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},
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{
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"feature": "co_citation_count",
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"importance": 2.4
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}
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],
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"key_insight": "V3 improves hard_neg_auc from 0.628 (CitationCount) to 0.738 (+17.5% relative). This means V3 can tell the difference between papers an expert included vs excluded 73.8% of the time, vs 62.8% for pure popularity. The n_surveys_citing feature dominates \u2014 cross-survey consensus is the strongest signal for paper importance beyond raw citation count."
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}
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