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**Statistical analysis of \(k\)-nearest neighbor collaborative recommendation.**
*(English)*
Zbl 1189.62190

Summary: Collaborative recommendation is an information-filtering technique that attempts to present information items that are likely of interest to an 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 the wide-ranging literature, little is known about the statistical properties of recommendation systems. In fact, no clear probabilistic model even exists which would allow 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. 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, and analyze its asymptotic performance as the number of users grows. We establish consistency of the procedure under mild assumptions on the model. Rates of convergence and examples are also provided.

### MSC:

62P99 | Applications of statistics |

62G08 | Nonparametric regression and quantile regression |

90B18 | Communication networks in operations research |

62G05 | Nonparametric estimation |

62G20 | Asymptotic properties of nonparametric inference |

62L99 | Sequential statistical methods |

65C60 | Computational problems in statistics (MSC2010) |

### Keywords:

collaborative recommendation; cosine-type similarity; nearest neighbor estimate; consistency; rate of convergence
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\textit{G. Biau} et al., Ann. Stat. 38, No. 3, 1568--1592 (2010; Zbl 1189.62190)

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