@inproceedings{shriver_evaluating_2019,
	title = {Evaluating {Recommender} {System} {Stability} with {Influence}-{Guided} {Fuzzing}},
	url = {https://www.comp.nus.edu.sg/~david/Publications/aaai2019-preprint.pdf},
	abstract = {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 …},
	publisher = {AAAI},
	author = {Shriver, David and Elbaum, Sebastian and Dwyer, Matthew B and Rosenblum, David S},
	year = {2019},
}

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