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CoBaFi - Collaborative Bayesian Filtering

机译：CoBaFi-协同贝叶斯过滤

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摘要

Given a large dataset of users' ratings of movies, what is the best model to accurately predict which movies a person will like? And how can we prevent spammers from tricking our algorithms into suggesting a bad movie? Is it possible to infer structure between movies simultaneously? In this paper we describe a unified Bayesian approach to Collaborative Filtering that accomplishes all of these goals. It models the discrete structure of ratings and is flexible to the often non-Gaussian shape of the distribution. Additionally, our method finds a co-clustering of the users and items, which improves the model's accuracy and makes the model robust to fraud. We offer three main contributions: (1) We provide a novel model and Gibbs sampling algorithm that accurately models the quirks of real world ratings, such as convex ratings distributions. (2) We provide proof of our model's robustness to spam and anomalous behavior. (3) We use several real world datasets to demonstrate the model's effectiveness in accurately predicting user's ratings, avoiding prediction skew in the face of injected spam, and finding interesting patterns in real world ratings data.
机译：给定大量用户对电影的评分的数据集，准确预测一个人喜欢的电影的最佳模型是什么？以及如何防止垃圾邮件发送者欺骗我们的算法来暗示一部不好的电影？是否可以同时推断电影之间的结构？在本文中，我们描述了实现所有这些目标的统一贝叶斯协作过滤方法。它可以对评级的离散结构进行建模，并且可以适应通常为非高斯分布的形状。此外，我们的方法可以找到用户和项目的共同集群，从而提高了模型的准确性，并使模型对欺诈具有鲁棒性。我们提供了三个主要的贡献：（1）我们提供了新颖的模型和Gibbs采样算法，可以准确地模拟现实世界评级（例如凸评级分布）的怪癖。 （2）我们提供了模型对垃圾邮件和异常行为的鲁棒性的证明。 （3）我们使用了几个真实世界的数据集来证明该模型在准确预测用户的评级，避免面对被注入的垃圾邮件时避免预测偏斜以及在现实世界的评级数据中找到有趣的模式方面的有效性。

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