# FOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning To Rank

arXiv: https://arxiv.org/abs/2206.07295 · HTML: https://mlsanity.com/paper/2206.07295/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 15 Jun 2022.

## Builds on (most cited first, 2 of 2)

- [A New Algorithm to Automate Inductive Learning of Default Theories](https://mlsanity.com/paper/1707.02693/) (2017)
- [Heuristic Based Induction of Answer Set Programs: From Default theories to combinatorial problems](https://mlsanity.com/paper/1802.06462/) (2018)

## Abstract (arXiv)

FOLD-R++ is a new inductive learning algorithm for binary classification tasks. It generates an (explainable) normal logic program for mixed type (numerical and categorical) data. We present a customized FOLD-R++ algorithm with the ranking framework, called FOLD-TR, that aims to rank new items following the ranking pattern in the training data. Like FOLD-R++, the FOLD-TR algorithm is able to handle mixed-type data directly and provide native justification to explain the comparison between a pair of items.