Improving recommendation diversity and identifying cultural biases for personalized ranking in large networks

Abstract

Personalized ranking and filtering algorithms, also known as recommender systems, form the backbone of many modern web applications. They are used to tailor and rank suggestions for users in search engines, e-commerce sites, social networks, and news aggregators. As such systems gain prevalence in people’s day-to-day lives, they also affect people’s behavior in several ways. Of the several concerns regarding these systems, the diversity of choices they offer to users is one of the important ones. Exposure to diverse items is consider Show more