Learning to Rank based on Analogical Reasoning

TL;DR

This paper introduces a novel object ranking method based on analogical reasoning principles, leveraging analogical proportions to transfer preferences across different domains, and demonstrates competitive performance in diverse datasets.

Contribution

It presents a new analogical reasoning-based approach for learning to rank, integrating instance-based learning and rank aggregation techniques.

Findings

Highly competitive results across various datasets

Effective knowledge transfer between different subdomains

Promising approach for preference learning with diverse data

Abstract

Object ranking or "learning to rank" is an important problem in the realm of preference learning. On the basis of training data in the form of a set of rankings of objects represented as feature vectors, the goal is to learn a ranking function that predicts a linear order of any new set of objects. In this paper, we propose a new approach to object ranking based on principles of analogical reasoning. More specifically, our inference pattern is formalized in terms of so-called analogical proportions and can be summarized as follows: Given objects $A,B,C,D$, if object $A$ is known to be preferred to $B$, and $C$ relates to $D$ as $A$ relates to $B$, then $C$ is (supposedly) preferred to $D$. Our method applies this pattern as a main building block and combines it with ideas and techniques from instance-based learning and rank aggregation. Based on first experimental results for data sets from various domains (sports, education, tourism, etc.), we conclude that our approach is highly competitive. It appears to be specifically interesting in situations in which the objects are coming from different subdomains, and which hence require a kind of knowledge transfer.