Abstract
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-generation pairing benefits GRU4Rec and SASRec, whereas FMLP initially favors within-generation pairing and shifts toward cross-generation pairing after a second update. Rank separation selects the stronger family in 12/12 first-update and 5/6 second-update dataset-encoder settings; on held-out tests, the selected family outperforms the direct successor in 34/36 trajectories. Five transfer mechanisms do not consistently reproduce these gains in one model. These findings establish state retention as a distinct Rec-RSI problem: progress may reside in relations between generations as well as in the latest model. Code is available at \href{https://github.com/Jinfeng-Xu/RecRSI}{https://github.com/Jinfeng-Xu/RecRSI}.
Keywords
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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Xu, J., Chen, Z., Peng, Z., Lin, Z., Yuan, W., Chen, J., Li, S., & Ngai, E. (2026). Beyond Successor Accuracy: State Retention for Recursive Self-Improvement in Recommendation. https://omanscience.com/en/articles/beyond-successor-accuracy-state-retention-for-recursive-self-improvement-in-recommendation
MLA 9
Xu, Jinfeng, et al. "Beyond Successor Accuracy: State Retention for Recursive Self-Improvement in Recommendation." https://omanscience.com/en/articles/beyond-successor-accuracy-state-retention-for-recursive-self-improvement-in-recommendation.
Chicago (author–date)
Xu, Jinfeng, Zheyu Chen, Ziyue Peng, Zheng Lin, Wenhao Yuan, Jian Chen, Shujie Li, and Edith Ngai. 2026. "Beyond Successor Accuracy: State Retention for Recursive Self-Improvement in Recommendation." https://omanscience.com/en/articles/beyond-successor-accuracy-state-retention-for-recursive-self-improvement-in-recommendation.
Harvard
Xu, J., Chen, Z., Peng, Z., Lin, Z., Yuan, W., Chen, J., Li, S. and Ngai, E. (2026) 'Beyond Successor Accuracy: State Retention for Recursive Self-Improvement in Recommendation', Available at: https://omanscience.com/en/articles/beyond-successor-accuracy-state-retention-for-recursive-self-improvement-in-recommendation.
Vancouver
Xu J, Chen Z, Peng Z, Lin Z, Yuan W, Chen J, et al. Beyond Successor Accuracy: State Retention for Recursive Self-Improvement in Recommendation. https://omanscience.com/en/articles/beyond-successor-accuracy-state-retention-for-recursive-self-improvement-in-recommendation
IEEE
J. Xu, Z. Chen, Z. Peng, Z. Lin, W. Yuan, J. Chen, S. Li, and E. Ngai, "Beyond Successor Accuracy: State Retention for Recursive Self-Improvement in Recommendation," https://omanscience.com/en/articles/beyond-successor-accuracy-state-retention-for-recursive-self-improvement-in-recommendation.