Adaptive computation depth per item, learned through gating decisions at each recursion step, outperforms fixed-depth processing in sequential recommendation by matching computational effort to actual item complexity.
This paper identifies that sequential recommendation systems waste computation by treating all items equally. The authors propose LoopRec, which uses adaptive recursion to allocate deeper processing only to items that need it—like recent interactions or rare items—while letting simpler items exit early. This improves recommendation quality without requiring manual depth labels.