Publications related to Filter bubble

On Artificial Intelligence and Manipulation

The increasing diffusion of novel digital and online sociotechnical systems for arational behavioral influence based on Artificial Intelligence (AI), such as social media, microtargeting advertising, and personalized search algorithms, has brought about ne ...

SPRINGER2023

Interacting with Explanations through Critiquing

Boi Faltings, Claudiu-Cristian Musat, Diego Matteo Antognini

Using personalized explanations to support recommendations has been shown to increase trust and perceived quality. However, to actually obtain better recommendations, there needs to be a means for users to modify the recommendation criteria by interacting ...

2021

Technologizing Democracy or Democratizing Technology? A Layered-Architecture Perspective on Potentials and Challenges

Bryan Alexander Ford

While technology is often claimed to be “democratizing”, the technologizing of society has more often yielded undemocratic or even anti-democratic outcomes. Is technology fundamentally at odds with democracy, or is it merely a rich and infinitely-adaptable ...

University of Chicago Press2021

Advances in Bias-aware Recommendation on the Web

Mirko Marras

The goal of this tutorial is to provide the WSDM community with recent advances on the assessment and mitigation of data and algorithmic bias in recommender systems. We first introduce conceptual foundations, by presenting the state of the art and describi ...

ASSOC COMPUTING MACHINERY2021

Controlling Polarization in Personalization: An Algorithmic Framework

Nisheeth Vishnoi, Farnood Salehi, Laura Elisa Celis, Sayash Kapoor

Personalization is pervasive in the online space as it leads to higher efficiency for the user and higher revenue for the platform by individualizing the most relevant content for each user. However, recent studies suggest that such personalization can lea ...

ASSOC COMPUTING MACHINERY2019

A dashboard for controlling polarization in personalization

Nisheeth Vishnoi, Farnood Salehi, Laura Elisa Celis, Vijay Keswani, Sayash Kapoor

Personalization is pervasive in the online space as it leads to higher efficiency for the user and higher revenue for the platform by individualizing the most relevant content for each user. However, recent studies suggest that such personalization can lea ...

IOS PRESS2019

CoFeed: privacy-preserving Web search recommendation based on collaborative aggregation of interest feedback

Martin Rajman, Vinh Toan Luu, Pascal Felber

Search engines essentially rely on the structure of the graph of hyperlinks. Although accurate for the main trend, this is not effective when some query is ambiguous. Leveraging semantic information by the mean of interest matching allows proposing complem ...

Wiley-Blackwell2013