用于无再训练推荐的可变低秩草图
Mutable Low-Rank Sketches for Retrain-Free Recommendation
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中文总结 AI 辅助
研究两阶段推荐中嵌入陈旧性问题，提出可变草图方法，将用户偏好存于KP树，一次拟合低秩投影，即时重算嵌入。证明可收紧预测误差范围，实验显示该方法在KuaiRec上效果好、更新快，新用户获推荐快，还比较了不同采样策略。
中文摘要 AI 辅助
两阶段推荐中的一个常见瓶颈是嵌入陈旧性：当用户对新项目评分时，其嵌入在下次再训练周期之前保持不变。我们提出了可变草图，它将每个用户的偏好存储在KP树（具有求和聚合的稀疏段树）中，一次拟合低秩投影，并在评分到达时即时重新计算嵌入。我们证明每个新观察值都会单调收紧预测误差范围（定理1），这是FunkSVD和eALS所缺乏的保证。在KuaiRec上，可变草图在读取1.8%数据时RMSE达到0.810，而ALS在读取100%数据时RMSE为0.822，且每批更新速度快8倍。新用户首次评分后不到1毫秒即可获得个性化推荐，无需模型再训练。不同密度情况下采样策略的比较表明，KP树的范数比例采样在稀疏数据（密度<1%）上提供的项目覆盖率比均匀采样高40-130%，而在密集矩阵上均匀采样就足够了。
英文摘要
A common bottleneck in two-stage recommendation is embedding staleness: when a user rates a new item, their embedding remains fixed until the next retrain cycle. We propose mutable sketches, which store each user's preferences in a KP-tree (a sparse segment tree with sum aggregation), fit a low-rank projection once, and recompute embeddings on-the-fly as ratings arrive. We prove that each new observation monotonically tightens the prediction error envelope (Theorem 1), a guarantee that FunkSVD and eALS lack. On KuaiRec, the mutable sketch achieves 0.810 RMSE at 1.8% data read vs. ALS 0.822 at 100%, with 8x faster per-batch updates. A new user receives personalized recommendations in <1 ms after their first rating, with no model retraining required. A comparison of sampling strategies across density regimes shows that the KP-tree's norm-proportional sampling provides 40-130% better item coverage on sparse data (<1% density), while uniform sampling suffices on dense matrices.
发表机构
- University of Michigan（密歇根大学）
- Criteo
机构由 AI 辅助整理，请以论文原文为准。
