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

Related publications (21)

Quantitative Methods for Omnichannel Decision-Making

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

Christina Fragouli, Suhas Diggavi, Pavlos Nikolopoulos

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

Discrete-Choice Mining of Social Processes

Victor Kristof

Poor decisions and selfish behaviors give rise to seemingly intractable global problems, such as the lack of transparency in democratic processes, the spread of conspiracy theories, and the rise in greenhouse gas emissions. However, people are more predict ...

EPFL2021

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

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

Efficient Learning from Comparisons

Lucas Maystre

Humans are comparison machines: comparing and choosing an item among a set of alternatives (such as objects or concepts) is arguably one of the most natural ways for us to express our preferences and opinions. In many applications, the analysis of data con ...

EPFL2018

Bayesian adaptation of chaos representations using variational inference and sampling on geodesics

Panagiotis Tsilifis

A novel approach is presented for constructing polynomial chaos representations of scalar quantities of interest (QoI) that extends previously developed methods for adaptation in Homogeneous Chaos spaces. In this work, we develop a Bayesian formulation of ...

ROYAL SOC2018

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

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

Scalable Collaborative Bayesian Preference Learning

Matthias Seeger, Mohammad Emtiyaz Khan, Young Jun Ko

Learning about users’ utilities from preference, discrete choice or implicit feedback data is of integral importance in e-commerce, targeted advertising and web search. Due to the sparsity and diffuse nature of data, Bayesian approaches hold much promise, ...

2014

Low-Rank Tensor Methods for Communicating Markov Processes

Daniel Kressner, Francisco Santos Paredes Quartin de Macedo

Stochastic models that describe interacting processes, such as stochastic automata networks, feature a dimensionality that grows exponentially with the number of processes. This state space explosion severely impairs the use of standard methods for the num ...

Springer-Verlag Berlin2014

Understanding and improving relational matrix factorization in recommender systems

Boi Faltings, Li Pu

Matrix factorization techniques such as the singular value decomposition (SVD) have had great success in recommender systems. We present a new perspective of SVD for constructing a latent space from the training data, which is justified by the theory of hy ...

ACM Press2013

Model-based automated testing of critical PLC programs

Simon Bliudze, Jean-Charles Tournier

Testing of critical PLC (Programmable Logic Controller) programs remains a challenging task for control system engineers as it can rarely be automated. This paper proposes a model-based approach which uses the BIP (Behavior, Interactions and Priorities) fr ...

IEEE2013

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

Volkan Cevher, Anastasios Kyrillidis, Quoc Tran Dinh

We propose an algorithmic framework for convex minimization problems of a composite function with two terms: a self-concordant function and a possibly nonsmooth regularization term. Our method is a new proximal Newton algorithm that features a local quadra ...

2013

Making Automated Testing of Cloud Applications an Integral Component of PaaS

George Candea, Johannes Kinder, Stefan Bucur

Traditional testing is inadequate for the complexity of modern cloud application software stacks. While the platform-as-a-service (PaaS) model has streamlined application development and deployment, its multiple abstraction layers and dependencies have mad ...

2013

Block-Floating-Point Enhanced MMSE Filter Matrix Computation for MIMO-OFDM Communication Systems

Andreas Peter Burg, Carl Christian Sten Dominic Senning

n this paper we present an architecture for an MMSE ﬁlter matrix computation unit for signal detection in MIMO-OFDM communication systems. We propose to compute the required matrix inverse based on a Cholesky decomposition, followed by a Gauss-Jordan matri ...

2013

The Metropolized Partial Importance Sampling MCMC mixes slowly on minimal reversal rearrangement paths

Krister Swenson

Markov chain Monte Carlo has been the standard technique for inferring the posterior distribution of genome rearrangement scenarios under a Bayesian approach. We present here a negative result on the rate of convergence of the generally used Markov chains. ...

2010

PathScore-Relevance: A Metric for Improving Test Quality

George Candea, Silviu Andrica

Reliability of today's software systems hinges on developers writing test cases that exercise as much of a program as possible. Writing and running such tests is inevitably subject to a time budget. This paper addresses the question of how to maximize qual ...

2009

Reduced basis method for multi-parameter dependent steady Navier-Stokes equations: applications to natural convection in a cavity

Gianluigi Rozza, Simone Deparis

This work focuses on the approximation of parametric steady Navier-- Stokes equations by the reduced basis method. For a particular instance of the parameters under consideration, we are able to solve the underlying partial differential equations, compute ...

2009

VLSI implementation of a high-speed iterative sorted MMSE QR decomposition

Andreas Peter Burg

The QR decomposition is an important, but often underestimated prerequisite for pseudo- or non-linear detection methods such as successive interference cancellation or sphere decoding for multiple-input multiple-output (MIMO) systems. The ability of concur ...

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