Why Should I Not Follow You? Reasons For and Reasons Against in Responsible Recommender Systems

EasyChair Preprint no. 4152

Abstract

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.

Keyphrases: Explainable Artificial Intelligence, Interpretable Machine Learning, Knowledge Graphs, Reasons Against, Recommender Systems

@Booklet{EasyChair:4152,
  author = {Gustavo P. Polleti and Douglas Luan de Souza and Fabio G. Cozman},
  title = {Why Should I Not Follow You?  Reasons For and Reasons Against in Responsible Recommender Systems},
  howpublished = {EasyChair Preprint no. 4152},

  year = {EasyChair, 2020}}