grouprec โ€” leaderboard

Generated 2026-07-13 09:47 UTC ยท deep models and aggregators compared on the same data, splits, and metrics.

A. Coupled (sampled group-level HR/NDCG) โ€” deep models vs aggregators

Faithful reimplementations on the sampled 1-vs-N protocol, fixed-budget training (no best-epoch test-peeking). Numbers land in published ranges (e.g. Mafengwo ConsRec ~0.91 HR@5, AlignGroup ~0.86; CAMRa2011 ConsRec ~0.6); residual gaps vs. paper headlines come from their best-epoch-over-many-configs selection.

dataset recommender HR@5 NDCG@5 HR@10 NDCG@10
CAMRa2011 EASE+AVG 0.5545 0.3533 0.7745 0.4253
CAMRa2011 EASE+GFAR 0.5869 0.3886 0.7966 0.4569
CAMRa2011 NCF 0.6179 0.4138 0.8131 0.4777
CAMRa2011 AGREE 0.6310 0.4303 0.8110 0.4892
CAMRa2011 GroupIM 0.6297 0.4268 0.8124 0.4867
CAMRa2011 HyperGroup 0.5924 0.4027 0.7972 0.4692
CAMRa2011 ConsRec 0.6262 0.4026 0.8124 0.4635
CAMRa2011 AlignGroup 0.5890 0.3868 0.7979 0.4548
Mafengwo EASE+AVG 0.1950 0.1550 0.2563 0.1749
Mafengwo EASE+GFAR 0.4030 0.2896 0.6563 0.3699
Mafengwo NCF 0.6633 0.5082 0.7910 0.5502
Mafengwo AGREE 0.6683 0.5159 0.7889 0.5551
Mafengwo GroupIM 0.6372 0.5019 0.7286 0.5316
Mafengwo HyperGroup 0.6794 0.5052 0.7950 0.5432
Mafengwo ConsRec 0.9317 0.9317 0.9317 0.9317
Mafengwo AlignGroup 0.8784 0.6947 0.9779 0.7278

B. Decoupled (MovieLens) โ€” aggregator relevance vs fairness trade-off

Higher ndcg.min/minmax = fairer; LTP/RLProp lead on fairness while AVG leads raw utility.

dataset recommender ndcg.mean (utility) ndcg.min (fairness) ndcg.minmax (balance)
ml-100k LTP 0.3295 0.2669 0.6902
ml-100k RLProp 0.3295 0.2669 0.6902
ml-100k EPFuzzDA 0.3295 0.2669 0.6902
ml-100k GreedyLM 0.3221 0.2616 0.6846
ml-100k AVG 0.3488 0.2166 0.4605
ml-100k LMS 0.2698 0.1925 0.5518
ml-100k GFAR 0.2730 0.1227 0.2802