Benoît Cadre

Nonparametric Spatial Prediction

Let (ℕ*)N be the integer lattice points in the N-dimensional Euclidean space. We define a nonparametric spatial predictor for the values of a random field indexed by (ℕ*)N using a kernel method. We first examine the general problem of the regression estimation for random fields. Then we show the uniform consistency on compact sets of our spatial predictor as well as its asymptotic normality.

Statistical analysis of k-nearest neighbor collaborative recommendation

Collaborative recommendation is an information-filtering technique that attempts to present information items (movies, music, books, news, images, Web pages, etc.) that are likely of interest to the Internet user. Traditionally, collaborative systems deal with situations with two types of variables, users and items. In its most common form, the problem is framed as trying to estimate ratings for items that have not yet been consumed by a user. Despite wide-ranging literature, little is known about the statistical properties of recommendation systems. In fact, no clear probabilistic model even exists allowing us to precisely describe the mathematical forces driving collaborative filtering. To provide an initial contribution to this, we propose to set out a general sequential stochastic model for collaborative recommendation and analyze its asymptotic performance as the number of users grows. We offer an in-depth analysis of the so-called cosine-type nearest neighbor collaborative method, which is one of the most widely used algorithms in collaborative filtering. We establish consistency of the procedure under mild assumptions on the model. Rates of convergence and examples are also provided.