Graphfusionsbr Eval

---
name: graphfusionsbr-eval
description: Evaluates session-based recommendation systems by predicting the next item in a user's interaction sequence. It probes the model's ability to capture high-order item relationships and leverage external knowledge graphs for accurate, context-aware ranking. Use when the user wants to benchmark on Tmall, RetailRocket, KKBox, or asks about evaluating this task. Reports P@10.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2601.08497
  bibtex_key: he2026graphfusionsbr
  confidence: high
---

# graphfusionsbr-eval

> GraphFusionSBR: Denoising Multi-Channel Graphs for Session-Based Recommendation — He et al. (2026) (arXiv:2601.08497, 2026)

## What this evaluates

Evaluates session-based recommendation systems by predicting the next item in a user's interaction sequence. It probes the model's ability to capture high-order item relationships and leverage external knowledge graphs for accurate, context-aware ranking.

## Datasets

- **Tmall** — total 374178; splits: train (348766), test (25412)
- **RetailRocket** — total 392010; splits: train (388541), test (3469)
- **KKBox** — total 514513; splits: train (432689), test (81824)

## Metrics

- `P@10` **(primary)** — range: percent
  - Precision@10 measures the proportion of correctly recommended items within the top 10 predictions. Calculated as the average of binary indicators over all test sessions.
- `MRR@10` — range: percent
  - Mean Reciprocal Rank@10 assesses ranking quality by taking the reciprocal of the rank of the first correct item in the top 10 list. Averaged over all test sessions.

## Input / output format

**Input**: A session prefix sequence of item IDs [i_1, ..., i_{m-1}] and associated knowledge graph attributes (e.g., category, seller, genre).

**Output**: A ranked list of candidate item IDs (top K).

## Scoring recipe

```python
def compute_metrics(preds, gold, K):
    p_at_k = 0.0
    mrr_at_k = 0.0
    for p, g in zip(preds, gold):
        if g in p[:K]:
            p_at_k += 1.0
            rank = p.index(g) + 1
            mrr_at_k += 1.0 / rank
    return p_at_k / len(gold) * 100, mrr_at_k / len(gold) * 100
```

## Common pitfalls

- Data augmentation via sequence splitting significantly inflates training set size (e.g., Tmall goes from 120k sessions to 348k sequences); failing to replicate this step will yield mismatched training scales.
- Dataset-specific filtering rules vary (e.g., RetailRocket retains sessions of length 2-4 and >4; KKBox filters tracks <1 min and enforces max order difference of 3); applying a uniform filter will corrupt the data distribution.
- Metrics are reported as percentages in tables but calculated as proportions; ensure consistent scaling when comparing results.

## Evidence (verbatim from paper)

> To evaluate the performance of our model, we employ two widely used metrics: Precision@$K$ (P@$K$) and Mean Reciprocal Rank@$K$ (MRR@$K$), with $K$ values set to 10 and 20. These metrics enable a robust assessment of both recommendation accuracy and ranking quality, which are essential for session-based recommendation systems. Specifically, P@$K$ measures the proportion of correctly recommended items within the top $K$ predictions.

## Citation

```bibtex
@misc{he2026graphfusionsbr,
  title={GraphFusionSBR: Denoising Multi-Channel Graphs for Session-Based Recommendation},
  author={He et al. (2026)},
  year={2026},
  note={arXiv:2601.08497}
}
```

- arXiv: 2601.08497