Faithfully explaining rankings in a news recommender system
arXiv preprint arXiv:1805.05447, 2018•arxiv.org
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 …
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 …
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.
arxiv.org
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