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Context-Tree Recommendation vs Matrix-Factorization: Algorithm Selection and Live Users Evaluation

Publications associées (32)

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Quantitative Methods for Omnichannel Decision-Making

Andrey Vasilyev

Omnichannel retail has emerged as the new standard in today's commerce landscape, with retailers integrating their physical and online channels to enhance the customer shopping experience. However, such integration presents significant challenges for retai ...

EPFL2023

Community-Aware Group Testing

Pavlos Nikolopoulos, Christina Fragouli, Suhas Diggavi, Sundara Rajan Srinivasavaradhan

Group testing is a technique that can reduce the number of tests needed to identify infected members in a population, by pooling together multiple diagnostic samples. Despite the variety and importance of prior results, traditional work on group testing ha ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Uniaxial fiber reinforced DEA fabrication

Julian Asboth

Dielectric Elastomer Actuators (DEA) are devices designed to convert electric energy into mechanical work. However, the current actuator design will expand when actuated while muscles contract. Fiber reinforcement may allow for anisotropic movement, which ...

2023

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

Enhancing Session-Based Recommendations through Sequential Modeling

Boi Faltings, Vincent Jean Fabrice Schickel, Stéphane Bernard Martin

Recommender systems typically determine the items they should recommend by learning models of user-preferences. Most often, those preferences are modeled as static and independent of context. In real life however, users consider items in sequence: TV serie ...

ASSOC COMPUTING MACHINERY2018

Enhancing Session-Based Recommendations through Sequential Modeling

Boi Faltings, Vincent Jean Fabrice Schickel, Stéphane Bernard Martin

ACM2018

MATHICSE Technical Report : Fast QR decomposition of HODLR matrices

Daniel Kressner, Ana Susnjara

The efficient and accurate QR decomposition for matrices with hierarchical low-rank structures, such as HODLR and hierarchical matrices, has been challenging. Existing structure-exploiting algorithms are prone to numerical instability as they proceed indi- ...

MATHICSE2018

Fast hierarchical solvers for symmetric eigenvalue problems

Ana Susnjara

In this thesis we address the computation of a spectral decomposition for symmetric banded matrices. In light of dealing with large-scale matrices, where classical dense linear algebra routines are not applicable, it is essential to design alternative tech ...

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

PBCOV: a property-based coverage criterion

Hamza Harkous, Kassem Fawaz

Coverage criteria aim at satisfying test requirements and compute metrics values that quantify the adequacy of test suites at revealing defects in programs. Typically, a test requirement is a structural program element, and the coverage metric value repres ...

Springer2015