A Batch Learning Framework for Scalable Personalized Ranking

Understand

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.