Publication

Towards a highly effective and robust Web credibility evaluation system

Publications associées (29)

Content Moderation in Online Platforms

A critical role of online platforms like Facebook, Wikipedia, YouTube, Amazon, Doordash, and Tinder is to moderate content. Interventions like banning users or deleting comments are carried out thousands of times daily and can potentially improve our onlin ...

EPFL2024

Referencing in YouTube Knowledge Communication Videos

Daniel Gatica-Perez, Haeeun Kim

In recent years, there has been widespread concern about misinformation and hateful content on social media that are damaging societies. Being one of the most influential social media that practically serves as a newsearch engine, YouTube has accepted crit ...

Assoc Computing Machinery2023

Dynamic Personalized Ranking

Jérémie Rappaz

Personalized ranking methods are at the core of many systems that learn to produce recommendations from user feedbacks. Their primary objective is to identify relevant items from very large vocabularies and to assist users in discovering new content. These ...

EPFL2022

A large scale study of reader interactions with images on Wikipedia

Tiziano Piccardi, Daniele Rama

Wikipedia is the largest source of free encyclopedic knowledge and one of the most visited sites on the Web. To increase reader understanding of the article, Wikipedia editors add images within the text of the article's body. However, despite their widespr ...

SPRINGER2022

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

Multi-Step Critiquing User Interface for Recommender Systems

Boi Faltings, Diego Matteo Antognini, Diana Andreea Petrescu

Recommendations with personalized explanations have been shown to increase user trust and perceived quality and help users make better decisions. Moreover, such explanations allow users to provide feedback by critiquing them. Several algorithms for recomme ...

ASSOC COMPUTING MACHINERY2021

p Recommender systems: Past, present, future

Pearl Pu Faltings

The origins of modern recommender systems date back to the early 1990s when they were mainly applied experimentally to personal email and information filtering. Today, 30 years later, personalized recommendations are ubiquitous and research in this highly ...

AMER ASSOC ARTIFICIAL INTELL2021

Building blocks for commodity augmented reality-based molecular visualization and modeling in web browsers

Luciano Andres Abriata

For years, immersive interfaces using virtual and augmented reality (AR) for molecular visualization and modeling have promised a revolution in the way how we teach, learn, communicate and work in chemistry, structural biology and related areas. However, m ...

2020

Change Detection and Notification of Web Pages: A Survey

Lakmal Buddika Meegahapola

The majority of currently available webpages are dynamic in nature and are changing frequently. New content gets added to webpages, and existing content gets updated or deleted. Hence, people find it useful to be alert for changes in webpages that contain ...

2020

Learning Urban Nightlife Routines from Mobile Data

Daniel Gatica-Perez, Thanh Trung Phan

The use of smartphone sensing for public health studies is appealing to understand routines. We present an approach to learn nightlife routines in a smartphone sensing dataset volunteered by 184 young people (1586 weekend nights with location data captured ...

ACM2020

Unified and Scalable Incremental Recommenders with Consumed Item Packs

Rachid Guerraoui, Rhicheek Patra

Recommenders personalize the web content using collaborative filtering to relate users (or items). This work proposes to unify user-based, item-based and neural word embeddings types of recommenders under a single abstraction for their input, we name Consu ...

Springer2019

Spherical Clustering of Users Navigating 360° Content

Pascal Frossard, Francesca De Simone, Laura Toni

In Virtual Reality (VR) applications, understanding how users explore the omnidirectional content is important to optimize content creation, to develop user-centric services, or even to detect disorders in medical applications. Clustering users based on th ...

IEEE2019

A Graph-structured Dataset for Wikipedia Research

Nicolas Aspert, Pierre Vandergheynst, Benjamin Ricaud, Volodymyr Miz

Wikipedia is a rich and invaluable source of information. Its central place on the Web makes it a particularly interesting object of study for scientists. Researchers from different domains used various complex datasets related to Wikipedia to study langua ...

ACM2019

Food Talks: visual and interaction principles for representing environmental and nutritional food information in augmented reality

Nicolas Henchoz, Delphine Ribes Lemay, Andreas Sonderegger, Emily Clare Groves

This user-centered design research project aimed to investigate visual and interaction principles for augmented reality (AR) in the context of environmental and nutritional food labelling. Nutritional information on existing food labels is often misunderst ...

2019

Why the World Reads Wikipedia: Beyond English Speakers

Robert West

As one of the Web's primary multilingual knowledge sources, Wikipedia is read by millions of people across the globe every day. Despite this global readership, little is known about why users read Wikipedia's various language editions. To bridge this gap, ...

ASSOC COMPUTING MACHINERY2019

Towards Scalable Personalization

Rhicheek Patra

The ever-growing amount of online information calls for Personalization. Among the various personalization systems, recommenders have become increasingly popular in recent years. Recommenders typically use collaborative filtering to suggest the most releva ...

EPFL2018

Privacy in Recommender Systems

Mahsa Taziki

The upsurge in the number of web users over the last two decades has resulted in a significant growth of online information. Recommenders are machine learning approach and are becoming one of the main ways to navigate the Internet. They recommend appropria ...

EPFL2018

Investigating users' eye movement behavior in critiquing-based recommender systems

Pearl Pu Faltings, Li Chen, Feng Wang

Recommender systems have increasingly become popular in various web environments (such as e-commerce and social media) for automatically generating items that match to individual users' personal interests. Among different types of recommender systems that ...

Ios Press2017

Enhancing Social Media Platforms for Educational and Humanitarian Knowledge Sharing : Analytics, Privacy, Discovery, and Delivery Aspects

Andrii Vozniuk

Social media (SM) platforms have demonstrated their ability to facilitate knowledge sharing on the global scale. They are increasingly often employed in educational and humanitarian domains where, despite their general benefits, they expose challenges pecu ...

EPFL2017

EHR: a Sensing Technology Readiness Model for Lifestyle Changes

Pearl Pu Faltings, Yu Chen, Danni Le, Zerrin Yumak

Interest in developing user-centered sensing technologies for personalized behavior change has gained significant momentum. However, very little research work has been done to understand issues relative to user readiness and adoption of the sensing technol ...

Springer2017