This work proposes Astar, a training-based approach that learns a specialized evolution-guiding model from the abundant iteration histories of industrial systems, and proposes a pipeline that turns noisy historical commits into a large, clean evolutionary corpus via pairwise sample expansion and noise filtering.
Jin-Xin Hu, Hao Deng, Hai-Bo Xing et al.· 2 citations
In e-commerce recommender systems, items that users have previously interacted with are frequently re-recommended, a practice known as retargeting recommendation. While retargeted items offer significantly higher conversion efficiency by leveraging verified user interests, their inherent predictability induces a system...
Jiang-Wei Deng, Xi-Ruo Shi, Li-Fang Deng et al.· Proceedings of the 20th ACM...· 0 citations
CMRec is proposed, a cross-country GR framework that injects cross-country supervision at the data level via dual-constrained, context-aware code-mixing that substantially improves recommendation quality in data-sparse countries while preserving performance in data-rich countries.
Yuan Gao, Hao Deng, Hai-Bo Xing et al.· 0 citations
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