Evaluating Recommender System Stability with Influence-Guided Fuzzing


  • David Shriver University of Virginia
  • Sebastian Elbaum University of Virginia
  • Matthew B. Dwyer University of Virginia
  • David S. Rosenblum National University of Singapore




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.




How to Cite

Shriver, D., Elbaum, S., Dwyer, M. B., & Rosenblum, D. S. (2019). Evaluating Recommender System Stability with Influence-Guided Fuzzing. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 4934-4942. https://doi.org/10.1609/aaai.v33i01.33014934



AAAI Technical Track: Machine Learning