Publications associées à PEN recsys: a Personalized News Recommender Systems Framework (DEMO)

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

Multi-Step Critiquing User Interface for Recommender Systems

Boi Faltings, Diana Andreea Petrescu, Diego Matteo Antognini

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

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

An open platform for Aerosol InfraRed Spectroscopy analysis - AIRSpec

Satoshi Takahama, Matteo Reggente, Rudolf Höhn

AIRSpec is a platform consisting of several chemometric packages developed for analysis of Fourier transform infrared (FTIR) spectra of atmospheric aerosols. The packages are accessible through a browser-based interface, which also generates the necessary ...

2019

Controlling Polarization in Personalization: An Algorithmic Framework

Nisheeth Vishnoi, Farnood Salehi, Laura Elisa Celis, Sayash Kapoor

Personalization is pervasive in the online space as it leads to higher efficiency for the user and higher revenue for the platform by individualizing the most relevant content for each user. However, recent studies suggest that such personalization can lea ...

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

Topics in Educational Cyber-Physical Labs : Configurations, Data Collection and Analysis

Wissam Halimi

Recent advances in remote sensing and actuation technologies, coupled with the large reach of the internet, allowed for the emergence of applications such as cyber-physical labs. Cyber-physical labs are the digital and remotely-accessible equivalent of the ...

EPFL2018

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

Probing the Limits of Social Data : Biases, Methods, and Domain Knowledge

Alexandra Olteanu

Online social data has been hailed to provide unprecedented insights into human phenomena due to its ability to capture human behavior at a scale and level of detail, both in breadth and depth, that is hard to achieve through conventional data collection t ...

EPFL2016

Personalizing Product Rankings Using Collaborative Filtering on Opinion-Derived Topic Profiles

Boi Faltings, Claudiu-Cristian Musat

Product review sites such as TripAdvisor, Yelp or Amazon provide a single, non personalized ranking of products. The sparse review data makes personalizing recommendations difficult. Topic Profile Collaborative Filtering exploits review texts to identify u ...

2015