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Add V3 eval metrics: nDCG@10=0.9494, hard_neg_auc=0.7380
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{
"model": "LightGBM LambdaRank V3",
"description": "Trained on cross-survey authority labels with 13 graph-based features",
"num_features": 13,
"features": [
"candidate_num_cited_by",
"candidate_num_references",
"survey_num_references",
"co_citation_count",
"bibliographic_coupling",
"jaccard_refs",
"adamic_adar",
"n_surveys_citing",
"n_total_citations",
"cited_by_survey_refs",
"candidate_cites_survey_refs",
"shared_citers_with_survey",
"hub_score"
],
"training": {
"train_rows": 58749,
"train_queries": 486,
"eval_rows": 1825,
"eval_queries": 14,
"best_iteration": 1,
"training_time_seconds": 0.4
},
"eval_metrics": {
"ndcg@10": 0.9515,
"ndcg@20": 0.9607,
"mrr": 0.9524,
"hard_neg_auc": 0.7364
},
"train_metrics": {
"ndcg@10": 0.9494,
"hard_neg_auc": 0.738
},
"comparison": {
"Random": {
"ndcg@10": 0.2356,
"hard_neg_auc": 0.5039
},
"CitationCount": {
"ndcg@10": 0.8371,
"hard_neg_auc": 0.6277
},
"Authority": {
"ndcg@10": 0.929,
"hard_neg_auc": 0.6463
},
"V3_LightGBM": {
"ndcg@10": 0.9494,
"hard_neg_auc": 0.738
},
"Oracle": {
"ndcg@10": 1.0,
"hard_neg_auc": 1.0
}
},
"feature_importance": [
{
"feature": "n_surveys_citing",
"importance": 1607.3
},
{
"feature": "hub_score",
"importance": 39.8
},
{
"feature": "survey_num_references",
"importance": 33.0
},
{
"feature": "adamic_adar",
"importance": 5.8
},
{
"feature": "candidate_num_references",
"importance": 3.3
},
{
"feature": "jaccard_refs",
"importance": 2.7
},
{
"feature": "co_citation_count",
"importance": 2.4
}
],
"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."
}