# Contextual Unsupervised Outlier Detection in Sequences

arXiv: https://arxiv.org/abs/2111.03808 · HTML: https://mlsanity.com/paper/2111.03808/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 43 references that are in the map.
- First posted on arXiv 6 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [TribeFlow: Mining & Predicting User Trajectories](https://mlsanity.com/paper/1511.01032/) (2015)
- [Social Bootstrapping: How Pinterest and Last.fm Social Communities Benefit by Borrowing Links from Facebook](https://mlsanity.com/paper/1402.6500/) (2014)

## Abstract (arXiv)

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.