Fairness-Aware Explainable Recommendation over Knowledge Graphs

SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval Virtual Event China July, 2020, pp. 69-78, 2020.

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We extensively evaluate our model on several real-world datasets, and demonstrate that our approach reduces unfairness by providing diverse path patterns and strong explainable recommendation results

Abstract:

There has been growing attention on fairness considerations recently, especially in the context of intelligent decision making systems. For example, explainable recommendation systems may suffer from both explanation bias and performance disparity. We show that inactive users may be more susceptible to receiving unsatisfactory recommendat...More

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