Freqrec Eval

freqrec-eval

Exploiting Inter-Session Information with Frequency-enhanced Dual-Path Networks for Sequential Recommendation — Peng He et al. (arXiv:2511.06285, 2025)

What this evaluates

Evaluates a sequential recommendation model's ability to predict the next item in a user's interaction history by jointly modeling intra-session and inter-session behavioral dynamics. It probes the model's recommendation accuracy, robustness to noisy cross-domain data, and stability under sparse interaction conditions.

Datasets

• Amazon Beauty — total ?; splits: train (-1), val (-1), test (-1)
• Sports & Outdoors — total ?; splits: train (-1), val (-1), test (-1)
• Toys & Games — total ?; splits: train (-1), val (-1), test (-1)

Metrics

• HR@K (primary) — range: [0, 1]
  • Binary indicator: 1 if the ground-truth next item appears in the top-K predicted items, 0 otherwise.
• NDCG@K (primary) — range: [0, 1]
  • Normalized Discounted Cumulative Gain for a single relevant item: 1/log2(2) if the ground-truth item is in the top-K, 0 otherwise.

Input / output format

Input: Ordered sequence of user-item interactions (sessions) representing historical behavior.

Output: Top-K ranked list of candidate items for the next interaction.

Scoring recipe

def compute_hr_ndcg(preds, gold, k):
    hit = 1.0 if gold in preds[:k] else 0.0
    dcg = hit / math.log2(2)
    idcg = 1.0 / math.log2(2)
    ndcg = dcg / idcg
    return hit, ndcg

Common pitfalls

• Data preprocessing and train/val/test splits are borrowed from prior work and not explicitly detailed in the main text.
• K values vary by experiment: K=10/20 for main benchmarks, but K=5 is used for sparsity and noise-resilience evaluations.
• Noise evaluation uses a cross-domain protocol (train on aggregate of Automotive, CDs, Grocery; test on each independently) rather than standard in-domain testing.

Evidence (verbatim from paper)

To comprehensively evaluate the model’s recommendation accuracy, we employ the top-$K$ evaluation method commonly used in SR, including Hit Rate (HR@$K$) and Normalized Discounted Cumulative Gain (NDCG@$K$), with $K$ set at 10 and 20.

Citation

@misc{he2025exploiting,
  title={Exploiting Inter-Session Information with Frequency-enhanced Dual-Path Networks for Sequential Recommendation},
  author={Peng He et al.},
  year={2025},
  note={arXiv:2511.06285}
}

• arXiv: 2511.06285