Publication

Aggregating information from the crowd : ratings, recommendations and predictions

Related publications (23)

Microwave Imaging of Brain Stroke : Contributions to Modeling and Inverse Problem Resolution

Brain stroke is an age-related illness which has become a major issue in our ageing societies. Early diagnosis and treatment are of high importance for the full recovery of the patient, as reminded in Anglo-Saxon countries by the abbreviation FAST (Face, A ...

EPFL2018

Structuring Wikipedia Articles with Section Recommendations

Robert West, Tiziano Piccardi, Michele Catasta

Sections are the building blocks of Wikipedia articles. They enhance readability and can be used as a structured entry point for creating and expanding articles. Structuring a new or already existing Wikipedia article with sections is a hard task for human ...

ASSOC COMPUTING MACHINERY2018

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

Context-Dependent Privacy-Aware Photo Sharing based on Machine Learning

Touradj Ebrahimi, Lin Yuan, Joël Régis Theytaz

Photo privacy has raised a growing concern with the advancements of image analytics, face recognition, and deep learning techniques widely applied on social media. If properly deployed, these powerful techniques can in turn assist people in enhancing their ...

2017

Learning Explainable User Sentiment and Preferences for Information Filtering

Nikolaos Pappas

In the last decade, online social networks have enabled people to interact in many ways with each other and with content. The digital traces of such actions reveal people's preferences towards online content such as news or products. These traces often res ...

EPFL2016

Collaborative Recurrent Neural Networks for Dynamic Recommender Systems

Matthias Grossglauser, Lucas Maystre, Young Jun Ko

Modern technologies enable us to record sequences of online user activity at an unprecedented scale. Although such activity logs are abundantly available, most approaches to recommender systems are based on the rating-prediction paradigm, ignoring temporal ...

2016

Combining Content Analytics and Activity Tracking to Identify User Interests and Enable Knowledge Discovery

Denis Gillet, Maria Jesus Rodriguez Triana, Adrian Christian Holzer, Andrii Vozniuk

Finding relevant content is one of the core activities of users interacting with a content repository, be it knowledge workers using an organizational knowledge management system at a workplace or self-regulated learners collaborating in a learning environ ...

2016

A 3-D Riesz-Covariance Texture Model for Prediction of Nodule Recurrence in Lung CT

Adrien Raphaël Depeursinge

This paper proposes a novel imaging biomarker of lung cancer relapse from 3-D texture analysis of CT images. Three-dimensional morphological nodular tissue properties are described in terms of 3-D Riesz-wavelets. The responses of the latter are aggregated ...

Institute of Electrical and Electronics Engineers2016

Automating Grammar Comparison

Viktor Kuncak, Mikaël Mayer, Ravichandhran Kandhadai Madhavan

We consider from a practical perspective the problem of checking equivalence of context-free grammars. We present techniques for proving equivalence, as well as techniques for finding counter-examples that establish non-equivalence. Among the key building ...

2015

Predicting Users' Motivations behind Location Check-Ins and Utility Implications of Privacy Protection Mechanisms

Jean-Pierre Hubaux, Kévin Clément Huguenin, Igor Bilogrevic, Reza Shokri, Stefan Mihaila

Location check-ins contain both geographical and semantic information about the visited venues, in the form of tags (e.g., “restaurant”). Such data might reveal some personal information about users beyond what they actually want to disclose, hence their p ...

2015