Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: Recommender systems leverage user demographic informa-<lb>tion, such as age, gender, etc., to personalize recommenda-<lb>tions and better place their targeted ads. Oftentimes, users<lb>do not volunteer this information due to privacy concerns, or<lb>due to a lack of initiative in filling out their online profiles.<lb>We illustrate a new threat in which a recommender learns<lb>private attributes of users who do not voluntarily disclose<lb>them. We design both passive and active attacks that so-<lb>licit ratings for strategically selected items, and could thus<lb>be used by a recommender system to pursue this hidden<lb>agenda. Our methods are based on a novel usage of Bayesian<lb>matrix factorization in an active learning setting. Evalua-<lb>tions on multiple datasets illustrate that such attacks are<lb>indeed feasible and use significantly fewer rated items than<lb>static inference methods. Importantly, they succeed without<lb>sacrificing the quality of recommendations to users.

Recommending with an Agenda: Active Learning of Private Attributes using Matrix Factorization