Reducing Offline Evaluation Bias in Recommendation Systems

Recommendation systems have been integrated into the majority of large online systems. They tailor those systems to individual users by filtering and ranking information according to user profiles. This adaptation process influences the way users interact with the system and, as a consequence, increases the difficulty of evaluating a recommendation algorithm with historical data (via offline evaluation). This paper analyses this evaluation bias and proposes a simple item weighting solution that reduces its impact. The efficiency of the proposed solution is evaluated on real world data extracted from Viadeo professional social network.Comment: 23rd annual Belgian-Dutch Conference on Machine Learning (Benelearn 2014), Bruxelles : Belgium (2014

Using the Mean Absolute Percentage Error for Regression Models

We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models. We show that finding the best model under the MAPE is equivalent to doing weighted Mean Absolute Error (MAE) regression. We show that universal consistency of Empirical Risk Minimization remains possible using the MAPE instead of the MAE.Comment: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium. 2015, Proceedings of the 23-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015

Reducing offline evaluation bias of collaborative filtering algorithms

Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles. It thus influences the way users interact with the system and, as a consequence, bias the evaluation of the performance of a recommendation algorithm computed using historical data (via offline evaluation). This paper presents a new application of a weighted offline evaluation to reduce this bias for collaborative filtering algorithms.Comment: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium. pp.137-142, 2015, Proceedings of the 23-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015

Identification of parameters in amplitude equations describing coupled wakes

We study the flow behind an array of equally spaced parallel cylinders. A system of Stuart-Landau equations with complex parameters is used to model the oscillating wakes. Our purpose is to identify the 6 scalar parameters which most accurately reproduce the experimental data of Chauve and Le Gal [{Physica D {\bf 58}}, pp 407--413, (1992)]. To do so, we perform a computational search for the minimum of a distance \calj. We define \calj as the sum-square difference of the data and amplitudes reconstructed using coupled equations. The search algorithm is made more efficient through the use of a partially analytical expression for the gradient $\nabla \cal J$. Indeed $\nabla \cal J$ can be obtained by the integration of a dynamical system propagating backwards in time (a backpropagation equation for the Lagrange multipliers). Using the parameters computed via the backpropagation method, the coupled Stuart-Landau equations accurately predicted the experimental data from Chauve and Le Gal over a correlation time of the system. Our method turns out to be quite robust as evidenced by using noisy synthetic data obtained from integrations of the coupled Stuart-Landau equations. However, a difficulty remains with experimental data: in that case the several sets of identified parameters are shown to yield equivalent predictions. This is due to a strong discretization or ``round-off" error arising from the digitalization of the video images in the experiment. This ambiguity in parameter identification has been reproduced with synthetic data subjected to the same kind of discretization.Comment: 25 pages uuencoded compressed PostScript file (58K) with 13 figures (155K in separated file) Submitted to Physica

Consistance de la minimisation du risque empirique pour l'optimisation de l'erreur relative moyenne

National audienceWe study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models. We show that finding the best model under the MAPE is equivalent to doing weighted Mean Absolute Error (MAE) regression. We also show that, under some asumptions, universal consistency of Empirical Risk Minimization remains possible using the MAPE.Nous nous intéressons au problème de la minimisation de l'erreur relative moyenne dans le cadre des modèles de régression. Nous montrons que l'optimisation de ce critère est équivalente à la minimisation de l'erreur absolue par régressions pondérées et que l'approche par minimisation du risque empirique est, sous certaines hypothèses, consistante pour la minimisation de ce critère

On the three-dimensional temporal spectrum of stretched vortices

The three-dimensional stability problem of a stretched stationary vortex is addressed in this letter. More specifically, we prove that the discrete part of the temporal spectrum is only associated with two-dimensional perturbations.Comment: 4 pages, RevTeX, submitted to PR

Stopping power of hot QCD plasma

The partonic energy loss has been calculated taking both the hard and soft contributions for all the $2 \to 2$ processes, revealing the importance of the individual channels. Cancellation of the intermediate separation scale has been exhibited. Subtleties related to the identical final state partons have properly been taken into account. The estimated collisional loss is compared with its radiative counter part. We show that there exists a critical energy ($E_c$) below which the collisional loss is more than its radiative counterpart. In addition, we present closed form formulas for both the collision probabilities and the stopping power ($dE/dx$)Comment: revised version, section added, 9pages with 5 figure