论文标题
同时学习神经协作过滤中的输入和参数
Simultaneous Learning of the Inputs and Parameters in Neural Collaborative Filtering
论文作者
论文摘要
基于神经网络的协作过滤系统着重于设计网络体系结构以学习更好的表示形式，同时将输入固定到用户/项目交互向量和/或ID。在本文中，我们首先表明输入的非零元素是可学习的参数，可以确定组合用户/项目嵌入的权重，并将其修复限制模型在学习表示表示方面的力量。然后，我们建议学习与神经网络参数共同输入的非零元素的值。我们分析了模型的复杂性和方法的经验风险，并证明学习输入会导致更好的概括结合。我们在几个现实世界数据集上的实验表明，即使使用具有较小层和参数的浅网络结构，我们的方法也优于最先进的方法。
Neural network-based collaborative filtering systems focus on designing network architectures to learn better representations while fixing the input to the user/item interaction vectors and/or ID. In this paper, we first show that the non-zero elements of the inputs are learnable parameters that determine the weights in combining the user/item embeddings, and fixing them limits the power of the models in learning the representations. Then, we propose to learn the value of the non-zero elements of the inputs jointly with the neural network parameters. We analyze the model complexity and the empirical risk of our approach and prove that learning the input leads to a better generalization bound. Our experiments on several real-world datasets show that our method outperforms the state-of-the-art methods, even using shallow network structures with a smaller number of layers and parameters.
