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

Key points

• (2) Suboptimality of fixed allocation: given oracle access to the target item, a non-uniform depth assignment consistently outranks the fixed one, indicating that existing uniform computation both wastes capacity on already-converged items and sacrifices overall recommendation quality.1
• To this end, we propose LoopRec.1
• LoopRec recursively applies a single weight-shared block and introduces a lightweight gate at each recursion boundary that makes a binary exit-or-continue decision for every item independently.1
• Further analysis reveals that LoopRec's learned allocation exhibits interpretable patterns aligned with our post-hoc oracle analysis: lower-frequency items and more recent interactions naturally receive deeper computation.1

Sources (2)

• [1]Think Harder Where It Matters: Item-Level Adaptive Recursion for Sequential Recommendation
  Hugging Face Daily Papers · Oct 21, 12:00 AM

  Existing sequential recommenders usually allocate a fixed computational budget to every item in a user's interaction history, regardless of the item's contribution to the final prediction.

  (2) Suboptimality of fixed allocation: given oracle access to the target item, a non-uniform depth assignment consistently outranks the fixed one, indicating that existing uniform computation both wastes capacity on already-converged items and sacrifices overall recommendation quality.

• [2]Think Harder Where It Matters: Item-Level Adaptive Recursion for Sequential Recommendation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 05:58 PM

Extractive summary: sentences quoted from the sources.