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A few Recommender Systems (RS) resort to explanations so as to enhance trustin recommendations. However, current techniques for explanation generation tendto strongly uphold the recommended products instead of presenting both reasonsfor and reasons against them. We argue that an RS can better enhance overalltrust and transparency by frankly displaying both kinds of reasons to users.Wehave developed such an RS by exploiting knowledge graphs and by applyingSnedegar's theory of practical reasoning. We show that our implemented RS hasexcellent performance and we report on an experiment with human subjects thatshows the value of presenting both reasons for and against, with significantimprovements in trust, engagement, and persuasion.

Why should I not follow you? Reasons For and Reasons Against in Responsible Recommender Systems