Challenging Recommendation Engines Evaluation Metrics and Mitigating Bias Problem of Information Cascades and Confirmation Biases

摘要

Digital knowledge gave birth to massive communication spaces, new access paths and new cleavages. Our experiment deals with the challenging issue of accessing this knowledge on the Internet. Computer scientists set up prediction algorithms and recommender engines. This way, knowledge access is partly automatized. Using a real-life dataset, our goal is to simulate the iterative behavior shift produced by most used recommender engines. On this basis, we show that in the context of recommendation, existing evaluation metrics are driven by prediction testing methods and we argue that ambiguity has to be raised between prediction and recommendation. Secondly, we propose alternative evaluation metrics for recommendation systems, targeting mitigating the bias problem of information cascades and confirmation biases.