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arXiv Machine Learning By Zhanshuo Cao, Jiakai Tang, Yuqi Zhou, Sunhao Dai, Jun Xu

Think Harder Where It Matters: Item-Level Adaptive Recursion for Sequential Recommendation

Read the original on arXiv Machine Learning →

The paper introduces LoopRec, an item‑level adaptive recursion mechanism for sequential recommendation that replaces the conventional fixed‑depth computation with a learned, per‑item depth assignment. LoopRec recursively applies a shared block and uses a lightweight gate to decide whether each item should exit or continue processing, allowing early exit for items that converge quickly while allocating deeper computation to critical items. Experiments on multiple backbone families show that LoopRec consistently outperforms fixed‑depth counterparts, with learned depth patterns aligning with lower‑frequency and more recent items.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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