Publications related to Recommender system

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

Finding Paths for Explainable MOOC Recommendation: A Learner Perspective

Tatjana Nazaretsky, Tanja Christina Käser Jacober, Jibril Albachir Frej

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

From scattered sources to comprehensive technology landscape : A recommendation-based retrieval approach

Karl Aberer, Chi Thang Duong

Mapping the technology landscape is crucial for market actors to take informed investment decisions. However, given the large amount of data on the Web and its subsequent information overload, manually retrieving information is a seemingly ineffective and ...

ELSEVIER2023

Robust Collaborative Learning with Linear Gradient Overhead

Rachid Guerraoui, John Stephan, Sadegh Farhadkhani, Le Nguyen Hoang, Nirupam Gupta, Rafaël Benjamin Pinot

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

PLMR2023

On Artificial Intelligence and Manipulation

Marcello Ienca

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

SAFETRANS: a system for 3D visibility estimation and cloud cover detection

The estimation of visibility is of significant importance in aviation safety and forms part of the measurements rou-tinely collected in real time to provide safety guidelines and decisions. Our work concerns the creation and imple-mentation of a lidar-base ...

2023

Recommender systems: Trends and frontiers

Pearl Pu Faltings

Recommender systems (RSs), as used by Netflix, YouTube, or Amazon, are one of the most compelling success stories of AI. Enduring research activity in this area has led to a continuous improvement of recommendation techniques over the years, and today's RS ...

AMER ASSOC ARTIFICIAL INTELL2022

Positive and Negative Critiquing for VAE-based Recommenders

Boi Faltings, Diego Matteo Antognini

Providing explanations for recommended items allows users to refine the recommendations by critiquing parts of the explanations. As a result of revisiting critiquing from the perspective of multimodal generative models, recent work has proposed M&Ms-VAE, w ...

2022

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

TEE-based decentralized recommender systems: The raw data sharing redemption

Anne-Marie Kermarrec, Rafael Pereira Pires, Akash Balasaheb Dhasade, Nevena Dresevic

Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between user profiles to make recommendations, but potentially expose private data. Fe ...

2022

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

Single-Trace Clustering Power Analysis of the Point-Swapping Procedure in the Three Point Ladder of Cortex-M4 SIKE

Aymeric Genet, Novak Kaluderovic

In this paper, the recommended implementation of the post-quantum key exchange SIKE for Cortex-M4 is attacked through power analysis with a single trace by clustering with the k-means algorithm the power samples of all the invocations of the elliptic curve ...

Springer, Cham2022

The standard operating procedure for Airmodus Particle Size Magnifier and nano-Condensation Nucleus Counter

Lubna Dada

Measurements of aerosol particles and clusters smaller than 3 nm in diameter are performed by many groups in order to detect recently formed or emitted nanoparticles and for studying the formation and early growth processes of aerosol particles. The Airmod ...

2022

A Critical Re-evaluation of Neural Methods for Entity Alignment

Robert West, Akhil Arora, Manuel Leone, Alberto Garcia Duran, Stefano Huber

Neural methods have become the de-facto choice for the vast majority of data analysis tasks, and entity alignment (EA) is no exception. Not surprisingly, more than 50 different neural EA methods have been published since 2017. However, surprisingly, an ana ...

ASSOC COMPUTING MACHINERY2022

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

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

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

Fast Multi-Step Critiquing for VAE-based Recommender Systems

Boi Faltings, Diego Matteo Antognini

Recent studies have shown that providing personalized explanations alongside recommendations increases trust and perceived quality. Furthermore, it gives users an opportunity to refine the recommendations by critiquing parts of the explanations. On one han ...

ASSOC COMPUTING MACHINERY2021

The Winner Takes it All: Geographic Imbalance and Provider (Un)fairness in Educational Recommender Systems

Mirko Marras

Educational recommender systems channel most of the research efforts on the effectiveness of the recommended items. While teachers have a central role in online platforms, the impact of recommender systems for teachers in terms of the exposure such systems ...

ASSOC COMPUTING MACHINERY2021

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