In designing personalized ranking algorithms, it is desirable to encourage a high precision at the top of the ranked list. Existing methods either seek a smooth convex surrogate for a non-smooth ranking metric or directly modify updating procedures to encourage top accuracy. In this work we point out that these methods do not scale well to a large-scale setting, and this is partly due to the inaccurate pointwise or pairwise rank estimation. We propose a new framework for personalized ranking. It uses batch-based rank estimators and smooth rank-sensitive loss functions. This new batch learning framework leads to more stable and accurate rank approximations compared to previous work. Moreover, it enables explicit use of parallel computation to speed up training. We conduct empirical evaluation on three item recommendation tasks. Our method shows consistent accuracy improvements over state-of-the-art methods. Additionally, we observe time efficiency advantages when data scale increases.

提出了一种新的基于批处理的排名估算器和平滑的排名敏感损失函数的个性化排名框架，该框架比先前的工作具有更稳定和准确的排名近似，并且可以通过显式使用并行计算来加速训练，在三个物品推荐任务上取得一致的准确性改进和时间效率优势。

可扩展个性化排名的批量学习框架