Why should I not follow you? Reasons For and Reasons Against in Responsible Recommender Systems
This addresses the need for more transparent and trustworthy recommender systems for users, though it is incremental as it builds on existing explanation techniques.
The paper tackles the problem of one-sided explanations in recommender systems by proposing a system that presents both reasons for and against recommendations, resulting in significant improvements in trust, engagement, and persuasion in human experiments.
A few Recommender Systems (RS) resort to explanations so as to enhance trust in recommendations. However, current techniques for explanation generation tend to strongly uphold the recommended products instead of presenting both reasons for and reasons against them. We argue that an RS can better enhance overall trust and transparency by frankly displaying both kinds of reasons to users.We have developed such an RS by exploiting knowledge graphs and by applying Snedegar's theory of practical reasoning. We show that our implemented RS has excellent performance and we report on an experiment with human subjects that shows the value of presenting both reasons for and against, with significant improvements in trust, engagement, and persuasion.