Year: 2021
Type: preprint
Abstract: We propose the use of probabilistic programming techniques to tackle the malicious user identification problem in a recommendation algorithm. Probabilistic programming provides numerous advantages over other techniques, including but not limited to providing a disentangled representation of how malicious users acted under a structured model, as well as allowing for the quantification of damage caused by malicious users. We show experiments in malicious user identification using a model of regular and malicious users interacting with a simple recommendation algorithm, and provide a novel simulation-based measure for quantifying the effects of a user or group of users on its dynamics.
Source: arXiv (Cornell University)
Authors: Gambardella, Andrew, State, Bogdan, Khan, Naeemullah, Tsourides, Leo, Torr, Philip H. S., Baydin, Atılım Güneş
Institution: University of Oxford
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3204312862