  This work proposes an unsupervised learning framework for trajectory
(sequence) outlier detection that combines ranking tests with user sequence
models. The overall framework identifies sequence outliers at a desired false
positive rate (FPR), in an otherwise parameter-free manner. We evaluate our
methodology on a collection of real and simulated datasets based on user
actions at the websites last.fm and msnbc.com, where we know ground truth, and
demonstrate improved accuracy over existing approaches. We also apply our
approach to a large real-world dataset of Pinterest and Facebook users, where
we find that users tend to re-share Pinterest posts of Facebook friends
significantly more than other types of users, pointing to a potential influence
of Facebook friendship on sharing behavior on Pinterest.
