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

Publications associées (32)

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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

Jean-Charles Tournier, Simon Bliudze

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, Quoc Tran Dinh, Anastasios Kyrillidis

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

Optimal Linear Fusion for Distributed Detection Via Semidefinite Programming

Ali H. Sayed

Consider the problem of signal detection via multiple distributed noisy sensors. We study a linear decision fusion rule of [Z. Quan, S. Cui, and A. H. Sayed, ¿Optimal Linear Cooperation for Spectrum Sensing in Cognitive Radio Networks,¿ IEEE J. Sel. Topics ...

IEEE2010

Bayesian variable order Markov models

Christos Dimitrakakis

We present a simple, effective generalisation of variable order Markov models to full online Bayesian estimation. The mechanism used is close to that employed in context tree weighting. The main contribution is the addition of a prior, conditioned on conte ...

JMLR2010

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

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 ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2007

Gaussian Process Belief Propagation

Matthias Seeger

The framework of graphical models is a cornerstone of applied Statistics, allowing for an intuitive graphical specification of the main features of a model, and providing a basis for general Bayesian inference computations though belief propagation (BP). I ...

MIT Press2007