Publications associées à Positive and Negative Critiquing for VAE-based Recommenders

Dynamic Personalized Ranking

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

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

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

Applications of Approximate Learning and Inference for Probabilistic Models

Young Jun Ko

We develop approximate inference and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider the ill-posed inverse problem of recovering an image from an underdeter ...

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

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

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

Adaptive sentiment-aware one-class collaborative filtering

Andrei Popescu-Belis, Nikolaos Pappas

This paper presents a novel application of sentiment analysis to recommender systems relying on explicit one-class user feedback (favorites or likes), namely joint models of unary feedback and sentiment of free-form user comments. This combination is achie ...

Elsevier2016

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

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

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

Aggregating information from the crowd : ratings, recommendations and predictions

Florent Frédéric Garcin

With an ever-growing amount of data generated on the web, aggregating information from the crowd into meaningful knowledge has become crucial to companies in order to create a competitive edge. Not only companies, but also organizations and governments can ...

EPFL2014

Design and User Perception Issues for Personality-Engaged Recommender Systems

Rong Hu

Recommender systems have emerged, as an intelligent information filtering tool, to help users effectively identify information items of interest from a set of overwhelming choices and provide personalized services. Most recommendation technologies typicall ...

EPFL2012

User Perceived Qualities and Acceptance of Recommender Systems : The Role of Diversity

Nicolas Jones

Recommender systems have become important, as users are faced with an ever-increasing amount of information available on internet. Much of the research work on the topic has been focused on recommendation techniques, aiming at improving the accuracy of rec ...

EPFL2010

Preserving Privacy in Collaborative Filtering through Distributed Aggregation of Offline Profiles

Jean-Pierre Hubaux, Pedram Pedarsani, Reza Shokri, Georgios Theodorakopoulos

In recommender systems, usually, a central server needs to have access to users' profiles in order to generate useful recommendations. Having this access, however, undermines the users' privacy. The more information is revealed to the server on the user-it ...

ACM2009

Ontology filtering

Vincent Jean Fabrice Schickel

With the ever growing importance of internet, people are becoming overwhelmed by information. More concretely, consider a situation where you find yourself with an evening alone and would like to rent a DVD to watch. For several reasons, this is a difficul ...

EPFL2007