Publications associées à p Recommender systems: Past, present, future

Publications associées (33)

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

EPFL2024

Machine learning-aided generative molecular design

Philippe Schwaller, Jeff Guo

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Nature Portfolio2024

Finding Paths for Explainable MOOC Recommendation: A Learner Perspective

Tanja Christina Käser Jacober, Jibril Albachir Frej, Tatjana Nazaretsky

The increasing availability of Massive Open Online Courses (MOOCs) has created a necessity for personalized course recommendation systems. These systems often combine neural networks with Knowledge Graphs (KGs) to achieve richer representations of learners ...

2023

Why is the winner the best?

Jian Wang, Ho Ling Li, Gabriel Girard, Adrien Raphaël Depeursinge

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really gener ...

Ieee Computer Soc2023

A Conceptual Framework for Integrating Conversational Agents in Digital Education

Juan Carlos Farah

The presence of conversational agents (or chatbots) in educational contexts has been steadily increasing over the past few years. Recent surveys have shown widespread interest in the use of chatbots in education, both for research and practice. Although th ...

EPFL2023

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

Boi Faltings, Panayiotis Danassis, Ljubomir Rokvic

Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present ...

2023

Towards Novel Evaluation Methods for Social Dialog Systems

Ekaterina Svikhnushina

Language has shaped human evolution and led to the desire to endow machines with language abilities. Recent advancements in natural language processing enable us to achieve this breakthrough in human-machine interaction. However, introducing conversational ...

EPFL2023

The Metaverse as a Virtual Model of Platform Urbanism: Its Converging AIoT, XReality, Neurotech, and Nanobiotech and Their Applications, Challenges, and Risks

Simon Elias Bibri

With their exponentially rising computational power, digital platforms are heralding a new era of hybrid intelligence. There has recently been much enthusiasm and hype that the Metaverse has the potential to unlock hybrid intelligence. This is premised on ...

MDPI2023

Device and system as human interactive surface

Jamie Paik, Fabio Zuliani

A force-feedback surface that creates and modulates distinctive profile and stiffnessto interact with a user in contact thereto, the surface being functionally independentto be used as a single module but can be customized to extend the application indiver ...

2023

Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations

Mirko Marras

Online education platforms play an increasingly important role in mediating the success of individuals' careers. Therefore, while building overlying content recommendation services, it becomes essential to guarantee that learners are provided with equal re ...

SPRINGER2022

A Two-Step Approach To Leverage Contextual Data: Speech Recognition In Air-Traffic Communications

Petr Motlicek, Juan Pablo Zuluaga Gomez

Automatic Speech Recognition (ASR), as the assistance of speech communication between pilots and air-traffic controllers, can significantly reduce the complexity of the task and increase the reliability of transmitted information. ASR application can lead ...

IEEE2022

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

Textual Explanations and Critiques in Recommendation Systems

Diego Matteo Antognini

Artificial intelligence and machine learning algorithms have become ubiquitous. Although they offer a wide range of benefits, their adoption in decision-critical fields is limited by their lack of interpretability, particularly with textual data. Moreover, ...

EPFL2022

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

Online Dating Quantification Practices: A Human-Machine Learning Process

Jessica Carolina Pidoux

The phenomenon of online dating via web and mobile phone applications involves several actors: graphical interfaces, developers, algorithmic systems for user matching, and users. These actors have been studied in parallel by the social sciences and by comp ...

EPFL2021

Smart Toys plus plus : Exploiting the Social Connectedness for Playing and Learning

Ayberk Özgür, Arzu Güneysu Özgür

Under the umbrella of smart toys, a myriad of interactive systems have addressed a variety of scenarios considering entertainment, education, sustainability, social and environmental learning through play. Tangibles and small toy robots prevail; but intera ...

ASSOC COMPUTING MACHINERY2021

Learning V1 Simple Cells with Vector Representation of Local Content and Matrix Representation of Local Motion

Yufan Ren

This paper proposes a representational model for image pairs such as consecutive video frames that are related by local pixel displacements, in the hope that the model may shed light on motion perception in primary visual cortex (V1). The model couples the ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2022

Interactive Digital Engagement With Visual Artworks and Cultural Artefacts Enhances User Aesthetic Experiences in the Laboratory and Museum

Martin Vetterli, Christine Mohr, Loïc Arnaud Baboulaz

Digital technologies reshape the way we interact with our environment, including with artworks. Advanced computational imaging solutions allow having extremely high-resolution digital reproductions of artworks outside museums, presumably increasing artwork ...

TAYLOR & FRANCIS INC2022

The Theory, Practice, and Ethical Challenges of Designing a Diversity-Aware Platform for Social Relations

Daniel Gatica-Perez, Lakmal Buddika Meegahapola

Diversity-aware platform design is a paradigm that responds to the ethical challenges of existing social media platforms. Available platforms have been criticized for minimizing users' autonomy, marginalizing minorities, and exploiting users' data for prof ...

ASSOC COMPUTING MACHINERY2021

Surviving but not thriving: Comparing primary, vocational and higher education teachers' experiences during the COVID-19 lockdown

Helena Kovacs, Emilie-Charlotte Monnier, Caroline Pulfrey

In this paper we examine the impacts of the global pandemic in 2020 on different levels of education system, particularly looking at the changes in teaching practice. The health emergency caused closure of schools, and online distance education became a te ...

2021