Dense Deep Crossing Network for Recommender System
首发时间:2018-12-21
Abstract:Manual feature engineering has been the key to the success of many predictive tasks of web applications. However, with the exponential increase in the variety and the volume of features, manual feature engineering comes with high cost. Factorization Machines are able to automatically learn the second-order feature interactions. However, FM models capture the non-linear structure of real-world data in an insufficient way. And recent work has shown that DNNs are able to learn higher-order interactions based on existing ones. In this paper, we propose a Deep Dense Crossing Network (DDCN) for recommender system. DDCN keeps the benefits of a DNN model and propose a novel dense crossing structure which connects each layer to every other layer in a feed-forward fashion. DDCN has several advantages: strengthen feature propagation, encourage feature reuse and implicitly learn feature crossing in an efficiently way. We evaluate the model on two datasets of hotel recommendation and clothes recommendation and our experimental results have demonstrated its superiority over the state-of-art algorithms on the recommendation dataset, in terms of model accuracy.
keywords: Deep Learning Neural Networks Recommender System DenseNet Crossing Features
点击查看论文中文信息
推荐系统的密集型深度交叉网络
摘要:手动特征工程是Web应用程序许多预测任务成功的关键。然而,随着功能的多样性和数量的指数增加,手动特征工程带来了高成本。分解机器能够自动学习二阶特征交互。然而,FM模型以不充分的方式捕获现实世界数据的非线性结构。最近的工作表明,DNN能够基于现有的互动来学习高阶交互。在本文中,我们为推荐系统提出了深密交叉网络(DDCN)。 DDCN保留了DNN模型的优点,并提出了一种新颖的密集交叉结构,以前馈的方式将每一层连接到每一层。 DDCN具有以下几个优点:加强特征传播,鼓励特征重用,并有效地隐式学习特征交叉。我们在酒店推荐和服装推荐的两个数据集上评估模型,并且我们的实验结果在模型准确性方面证明了其优于推荐数据集的最新算法。
基金:
引用
No.****
动态公开评议
共计0人参与
勘误表
推荐系统的密集型深度交叉网络
评论
全部评论0/1000