Beyond Successor Accuracy: State Retention for Recursive Self-Improvement in Recommendation
Recommendation recursive self-improvement (Rec-RSI) feeds recommender outputs into subsequent training. Evaluating each round solely through its latest model assumes that the successor consolidates the update, although pre- and post-update models may retain complementary ranking decisions. We term this \emph{distributed progress} and quantify it using cross-generation advantage (CGA), a marginally matched contrast between cross- and within-generation model pairs. A rank-separation statistic, label-free at selection time, predicts which family to retain. Across four datasets and three sequential recommendation encoders, the preferred retention regime varies by architecture: cross-generatio...
| Authors | Jinfeng Xu, Zheyu Chen, Ziyue Peng et al. |
|---|---|
| Primary category | cs.IR |
| Published | 5 October 2026 |
| Last updated at source | 5 October 2026 |
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