A no-training graph heuristic beats generative recommenders on 10 of 14 benchmarks, exposing shortcut-solvable datasets. Relative NDCG@10 gains hit 44% on Amazon CDs.

A no-training graph heuristic beat generative recommenders on 10 of 14 benchmarks, per a May 8 arXiv preprint. The paper audited standard sequential recommendation datasets and found them shortcut-solvable.

Key facts

Heuristic uses only last 1-2 items, no training, no sequence encoder.

38.10% NDCG@10 gain on Amazon Review Sports.

44.18% NDCG@10 gain on Amazon Review CDs.

Competitive on 10 of 14 standard benchmarks.

Three shortcut structures identified: low-branching, feature-smooth, short history.

A new arXiv preprint (Han et al., May 8 2026) drops a grenade into the sequential recommendation literature: an embarrassingly simple graph heuristic, using only the last one or two interacted items, matches or outperforms many modern generative recommenders on 10 of 14 standard benchmarks.…