论文标题
通过推荐系统的潜在不变约束，一种简单且可扩展的张量完成算法
A Simple and Scalable Tensor Completion Algorithm via Latent Invariant Constraint for Recommendation System
论文作者
论文摘要
在本文中，我们根据基本属性为建议系统（RS）问题提供了潜在的可变量公式和解决方案，该属性应期望任何合理的解决方案满足任何合理的解决方案。具体而言，我们检查了一种新颖的张量完成方法，以有效，准确地学习模型的参数，以确保用户评级的不可观察的个人偏好。通过单个潜在不变式将张量分解正规化，我们为可靠的推荐系统实现了三个属性：（1）张量完成结果的唯一性，具有最小的假设，（2）独立于用户的任意偏好的单位一致性，以及（3）提供一致的订购保证，可以在观察和ubssed scorss scorss scors中提供一致的排名。我们的算法导致一个简单而优雅的推荐框架，具有线性计算复杂性，没有超参数调整。我们提供的经验结果表明，该方法明显胜过当前最新方法。
In this paper we provide a latent-variable formulation and solution to the recommender system (RS) problem in terms of a fundamental property that any reasonable solution should be expected to satisfy. Specifically, we examine a novel tensor completion method to efficiently and accurately learn parameters of a model for the unobservable personal preferences that underly user ratings. By regularizing the tensor decomposition with a single latent invariant, we achieve three properties for a reliable recommender system: (1) uniqueness of the tensor completion result with minimal assumptions, (2) unit consistency that is independent of arbitrary preferences of users, and (3) a consensus ordering guarantee that provides consistent ranking between observed and unobserved rating scores. Our algorithm leads to a simple and elegant recommendation framework that has linear computational complexity and with no hyperparameter tuning. We provide empirical results demonstrating that the approach significantly outperforms current state-of-the-art methods.
