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In designing personalized ranking algorithms, it is desirable to encourage ahigh precision at the top of the ranked list. Existing methods either seek asmooth convex surrogate for a non-smooth ranking metric or directly modifyupdating procedures to encourage top accuracy. In this work we point out thatthese methods do not scale well to a large-scale setting, and this is partlydue to the inaccurate pointwise or pairwise rank estimation. We propose a newframework for personalized ranking. It uses batch-based rank estimators andsmooth rank-sensitive loss functions. This new batch learning framework leadsto 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 methodshows consistent accuracy improvements over state-of-the-art methods.Additionally, we observe time efficiency advantages when data scale increases.

A Batch Learning Framework for Scalable Personalized Ranking