{
  "paper": {
    "arxiv_id": "2603.00980",
    "title": "Beyond the Flat Sequence: Hierarchical and Preference-Aware Generative Recommendations",
    "url": "https://arxiv.org/abs/2603.00980",
    "organization": "Harbin Institute of Technology"
  },
  "dataset": {
    "name": "MovieLens 100K",
    "users": 220,
    "items": 360
  },
  "setup": {
    "adapter": "hpgr",
    "same_split_and_candidates": true
  },
  "baseline": {
    "name": "transition + content + popularity",
    "hit_at_10": 0.10909090909090909,
    "ndcg_at_10": 0.05400546778896325,
    "fresh_hit_at_10": 0.031746031746031744,
    "head_share_at_10": 0.23772727272727273
  },
  "method": {
    "name": "hpgr core mechanism (validation blend=0.1)",
    "hit_at_10": 0.10454545454545454,
    "ndcg_at_10": 0.054668954918521265,
    "fresh_hit_at_10": 0.031746031746031744,
    "head_share_at_10": 0.19227272727272726
  },
  "relative": {
    "hit_at_10_percent": -4.166666666666668,
    "ndcg_at_10_percent": 1.2285554717361544,
    "fresh_hit_at_10_percent": 0.0,
    "head_share_at_10_percent": -19.12045889101339
  },
  "stages": {
    "finite_scores": 360,
    "score_std": 0.13877903609109107,
    "hierarchical_sessions": 4,
    "sparse_attention_keep_ratio": 0.3
  },
  "paper_results": {
    "ecpm_lift_percent": 1.99
  },
  "scope": "执行论文可由公开 MovieLens 特征审计的核心计算；不复刻私有日志、生产大模型或线上服务栈。",
  "manifest_ref": "reproduction:hpgr",
  "schema_version": 2,
  "manifest": {
    "adapter_key": "hpgr",
    "arxiv_id": "2603.00980",
    "title": "Beyond the Flat Sequence: Hierarchical and Preference-Aware Generative Recommendations",
    "paper_url": "https://arxiv.org/abs/2603.00980",
    "track": "recommendation",
    "organization": "Harbin Institute of Technology",
    "published": "2026-03-01",
    "code_url": null,
    "topics": [
      "generative-recommendation",
      "hierarchical-sequence",
      "sparse-attention"
    ],
    "local_code_dir": "src/auto_research/reproductions/hpgr",
    "fidelity": "core_mechanism",
    "evaluation_tier": "l2_public_dataset",
    "datasets": [
      "MovieLens 100K"
    ],
    "baseline": "transition + content + popularity",
    "metrics": [
      "hit_at_10",
      "ndcg_at_10",
      "fresh_hit_at_10",
      "head_share_at_10"
    ],
    "default_seeds": [
      42,
      43,
      44
    ],
    "budget": "220 users / 360 items; validation-only blend selection",
    "device_capabilities": [
      "cpu"
    ],
    "requires_gpu_validation": false,
    "gpu_validation_artifact": null,
    "online_evidence": [
      {
        "product": "Huawei AppGallery recommendation",
        "metric": "eCPM",
        "lift_percent": 1.99,
        "traffic": "production online A/B",
        "source_url": "https://arxiv.org/html/2603.00980v1",
        "source_location": "Section 1 and online experiments",
        "retrieved_at": "2026-09-05"
      }
    ],
    "selection_exception": null,
    "evolve_operators": [
      "context:hierarchical-preference"
    ]
  },
  "provenance": {
    "created_at": "2026-09-05T03:39:14.598759+00:00",
    "code_commit": "f4c21e438142960e37630c118f2dfb2e8a674004",
    "python": "3.12.9",
    "platform": "macOS-26.5.2-arm64-arm-64bit",
    "dataset_dir": "/Users/bytedance/Documents/git_daiwk/auto-research/data",
    "dataset_fingerprint": "e66c261317a7b3179720ef3e3f5f79d7a204b135d1f217e2911b153ad2ce50a0",
    "packages": {
      "auto-research": "0.1.0",
      "numpy": "2.5.2",
      "torch": "2.13.0",
      "transformers": "5.14.1"
    },
    "artifact_path": "docs/reproductions/2603.00980-hpgr/metrics/public-seeds42-44.json"
  },
  "evaluation_protocol": {
    "tier": "l2_public_dataset",
    "tier_label": "L2 公开数据集训练",
    "seeds": [
      42,
      43,
      44
    ],
    "budget": "paper-specific",
    "formal_comparison": true,
    "claim_policy": "formal multi-seed comparison"
  },
  "seed_results": [
    {
      "paper": {
        "arxiv_id": "2603.00980",
        "title": "Beyond the Flat Sequence: Hierarchical and Preference-Aware Generative Recommendations",
        "url": "https://arxiv.org/abs/2603.00980",
        "organization": "Harbin Institute of Technology"
      },
      "dataset": {
        "name": "MovieLens 100K",
        "users": 220,
        "items": 360
      },
      "setup": {
        "adapter": "hpgr",
        "same_split_and_candidates": true
      },
      "baseline": {
        "name": "transition + content + popularity",
        "hit_at_10": 0.10909090909090909,
        "ndcg_at_10": 0.05400546778896325,
        "fresh_hit_at_10": 0.031746031746031744,
        "head_share_at_10": 0.23772727272727273
      },
      "method": {
        "name": "hpgr core mechanism (validation blend=0.1)",
        "hit_at_10": 0.10454545454545454,
        "ndcg_at_10": 0.054668954918521265,
        "fresh_hit_at_10": 0.031746031746031744,
        "head_share_at_10": 0.19227272727272726
      },
      "relative": {
        "hit_at_10_percent": -4.166666666666668,
        "ndcg_at_10_percent": 1.2285554717361544,
        "fresh_hit_at_10_percent": 0.0,
        "head_share_at_10_percent": -19.12045889101339
      },
      "stages": {
        "finite_scores": 360,
        "score_std": 0.13877903609109107,
        "hierarchical_sessions": 4,
        "sparse_attention_keep_ratio": 0.3
      },
      "paper_results": {
        "ecpm_lift_percent": 1.99
      },
      "scope": "执行论文可由公开 MovieLens 特征审计的核心计算；不复刻私有日志、生产大模型或线上服务栈。",
      "manifest_ref": "reproduction:hpgr",
      "seed": 42
    },
    {
      "paper": {
        "arxiv_id": "2603.00980",
        "title": "Beyond the Flat Sequence: Hierarchical and Preference-Aware Generative Recommendations",
        "url": "https://arxiv.org/abs/2603.00980",
        "organization": "Harbin Institute of Technology"
      },
      "dataset": {
        "name": "MovieLens 100K",
        "users": 220,
        "items": 360
      },
      "setup": {
        "adapter": "hpgr",
        "same_split_and_candidates": true
      },
      "baseline": {
        "name": "transition + content + popularity",
        "hit_at_10": 0.10909090909090909,
        "ndcg_at_10": 0.05400546778896325,
        "fresh_hit_at_10": 0.031746031746031744,
        "head_share_at_10": 0.23772727272727273
      },
      "method": {
        "name": "hpgr core mechanism (validation blend=0.1)",
        "hit_at_10": 0.10454545454545454,
        "ndcg_at_10": 0.054668954918521265,
        "fresh_hit_at_10": 0.031746031746031744,
        "head_share_at_10": 0.19227272727272726
      },
      "relative": {
        "hit_at_10_percent": -4.166666666666668,
        "ndcg_at_10_percent": 1.2285554717361544,
        "fresh_hit_at_10_percent": 0.0,
        "head_share_at_10_percent": -19.12045889101339
      },
      "stages": {
        "finite_scores": 360,
        "score_std": 0.13877903609109107,
        "hierarchical_sessions": 4,
        "sparse_attention_keep_ratio": 0.3
      },
      "paper_results": {
        "ecpm_lift_percent": 1.99
      },
      "scope": "执行论文可由公开 MovieLens 特征审计的核心计算；不复刻私有日志、生产大模型或线上服务栈。",
      "manifest_ref": "reproduction:hpgr",
      "seed": 43
    },
    {
      "paper": {
        "arxiv_id": "2603.00980",
        "title": "Beyond the Flat Sequence: Hierarchical and Preference-Aware Generative Recommendations",
        "url": "https://arxiv.org/abs/2603.00980",
        "organization": "Harbin Institute of Technology"
      },
      "dataset": {
        "name": "MovieLens 100K",
        "users": 220,
        "items": 360
      },
      "setup": {
        "adapter": "hpgr",
        "same_split_and_candidates": true
      },
      "baseline": {
        "name": "transition + content + popularity",
        "hit_at_10": 0.10909090909090909,
        "ndcg_at_10": 0.05400546778896325,
        "fresh_hit_at_10": 0.031746031746031744,
        "head_share_at_10": 0.23772727272727273
      },
      "method": {
        "name": "hpgr core mechanism (validation blend=0.1)",
        "hit_at_10": 0.10454545454545454,
        "ndcg_at_10": 0.054668954918521265,
        "fresh_hit_at_10": 0.031746031746031744,
        "head_share_at_10": 0.19227272727272726
      },
      "relative": {
        "hit_at_10_percent": -4.166666666666668,
        "ndcg_at_10_percent": 1.2285554717361544,
        "fresh_hit_at_10_percent": 0.0,
        "head_share_at_10_percent": -19.12045889101339
      },
      "stages": {
        "finite_scores": 360,
        "score_std": 0.13877903609109107,
        "hierarchical_sessions": 4,
        "sparse_attention_keep_ratio": 0.3
      },
      "paper_results": {
        "ecpm_lift_percent": 1.99
      },
      "scope": "执行论文可由公开 MovieLens 特征审计的核心计算；不复刻私有日志、生产大模型或线上服务栈。",
      "manifest_ref": "reproduction:hpgr",
      "seed": 44
    }
  ],
  "aggregate_metrics": {
    "dataset.users": {
      "mean": 220.0,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "dataset.items": {
      "mean": 360.0,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "baseline.hit_at_10": {
      "mean": 0.10909090909090909,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "baseline.ndcg_at_10": {
      "mean": 0.05400546778896325,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "baseline.fresh_hit_at_10": {
      "mean": 0.031746031746031744,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "baseline.head_share_at_10": {
      "mean": 0.2377272727272727,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "method.hit_at_10": {
      "mean": 0.10454545454545454,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "method.ndcg_at_10": {
      "mean": 0.05466895491852127,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "method.fresh_hit_at_10": {
      "mean": 0.031746031746031744,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "method.head_share_at_10": {
      "mean": 0.19227272727272726,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "relative.hit_at_10_percent": {
      "mean": -4.166666666666668,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "relative.ndcg_at_10_percent": {
      "mean": 1.2285554717361544,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "relative.fresh_hit_at_10_percent": {
      "mean": 0.0,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "relative.head_share_at_10_percent": {
      "mean": -19.12045889101339,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "stages.finite_scores": {
      "mean": 360.0,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "stages.score_std": {
      "mean": 0.13877903609109107,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "stages.hierarchical_sessions": {
      "mean": 4.0,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "stages.sparse_attention_keep_ratio": {
      "mean": 0.3,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "paper_results.ecpm_lift_percent": {
      "mean": 1.99,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    }
  }
}