Perceptron-like Algorithms and Generalization Bounds for Learning to Rank

Understand

Learning to rank is a supervised learning problem where the output space is the space of rankings but the supervision space is the space of relevance scores.

• We make theoretical contributions to the learning to rank problem both in the online and batch settings.
• First, we propose a perceptron-like algorithm for learning a ranking function in an online setting.
• Our algorithm is an extension of the classic perceptron algorithm for the classification problem.