Evaluating Recommender System Stability with Influence-Guided Fuzzing


David Shriver, Sebastian Elbaum, Matthew B. Dwyer, David S. Rosenblum

Publication Date:

31 January 2019


AAAI Conference on Artificial Intelligence


Recommender systems help users to find products or services they may like when lacking personal experience or facing an overwhelming set of choices. Since unstable recommendations can lead to distrust, loss of profits, and a poor user experience, it is important to test recommender system stability. In this work, we present an approach based on inferred models of influence that underlie recommender systems to guide the generation of dataset modifications to assess a recommender's stability. We implement our approach and evaluate it on several recommender algorithms using the MovieLens dataset. We find that influence-guided fuzzing can effectively find small sets of modifications that cause significantly more instability than random approaches.


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