Think Harder Where It Matters: Item-Level Adaptive Recursion for Sequential Recommendation
[cs.IR] Autorzy: Zhanshuo Cao, Jiakai Tang, Yuqi Zhou i 2 więcej
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. We challenge this de facto design with two diagnostic findings. (1) Non-uniform computational need: items within the same sequence vary drastically in how much computation they require. Most converge after shallow processing while a critical minority only stabilizes at full depth. (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. These observations motivate a shift from fixed-depth computation to item-level adaptive reasoning. To this end, we propose LoopRec. LoopRec r…
- Kategoria arxiv
- cs.IR
- Kategoria
- cs.IR