The Graph Cut Kernel for Ranked Data

TL;DR

This paper introduces a graph cut kernel for ranked data that improves computational efficiency and scalability for real-world applications like recommender systems by leveraging geometric structure and submodular optimization.

Contribution

The paper proposes a novel graph cut kernel that efficiently handles partial and large-scale ranked data, combining submodular optimization with kernel methods.

Findings

Kernel enables scalable ranking analysis

Efficient handling of partial rankings

Theoretically grounded and computationally effective

Abstract

Many algorithms for ranked data become computationally intractable as the number of objects grows due to the complex geometric structure induced by rankings. An additional challenge is posed by partial rankings, i.e. rankings in which the preference is only known for a subset of all objects. For these reasons, state-of-the-art methods cannot scale to real-world applications, such as recommender systems. We address this challenge by exploiting the geometric structure of ranked data and additional available information about the objects to derive a kernel for ranking based on the graph cut function. The graph cut kernel combines the efficiency of submodular optimization with the theoretical properties of kernel-based methods. The graph cut kernel combines the efficiency of submodular optimization with the theoretical properties of kernel-based methods.