Faithfully Explaining Rankings in a News Recommender System

Maartje ter Hoeve and Anne Schuth and Daan Odijk and Maarten de Rijke. 2018.

Abstract

There is an increasing demand for algorithms to explain their outcomes. So far, there is no method that explains the rankings produced by a ranking algorithm. To address this gap we propose LISTEN, a LISTwise ExplaiNer, to explain rankings produced by a ranking algorithm. To efficiently use LISTEN in production, we train a neural network to learn the underlying explanation space created by LISTEN; we call this model Q-LISTEN. We show that LISTEN produces faithful explanations and that Q-LISTEN is able to learn these explanations. Moreover, we show that LISTEN is safe to use in a real world environment: users of a news recommendation system do not behave significantly differently when they are exposed to explanations generated by LISTEN instead of manually generated explanations.

Links

Faithfully Explaining Rankings in a News Recommender System
https://arxiv.org/abs/1805.05447

Bib

@article{terhoeve2018,
  title = {Faithfully Explaining Rankings in a News Recommender System},
  author = {Maartje ter Hoeve and Anne Schuth and Daan Odijk and Maarten de Rijke},
  year = {2018}
}