Faithfully Explaining Rankings in a News Recommender System
This addresses the problem of providing transparent explanations for ranking outcomes in news recommendation systems, though it is incremental as it builds on existing explanation needs without a major paradigm shift.
The authors tackled the lack of methods to explain rankings from ranking algorithms by proposing LISTEN, a listwise explainer, and Q-LISTEN, a neural network to learn its explanations, showing that LISTEN produces faithful explanations and is safe for real-world use with no significant user behavior changes compared to manual explanations.
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.