A Batch Learning Framework for Scalable Personalized Ranking
This work addresses scalability issues in personalized ranking for recommendation systems, offering incremental improvements in accuracy and efficiency.
The paper tackles the problem of scaling personalized ranking algorithms to large datasets by proposing a batch learning framework that uses batch-based rank estimators and smooth rank-sensitive loss functions, resulting in consistent accuracy improvements and time efficiency advantages over state-of-the-art methods on three item recommendation tasks.
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.