基于双交叉深度网络的新闻推荐算法
N: Double Cross & Deep Network for News Recommandation
推荐系统在互联网产品中的应用日渐广泛,推荐算法也愈发得到研究者们的关注,不断有新的推荐算法被提出以提高推荐内容的点击率。本文提出Double Cross & Deep Network(DCDN)算法,应用于新闻推荐当中。该算法在DCN网络的基础上,提出一种新的双交叉深度网络,将推荐候选集中的"相关新闻"的特征单独进行提取,并与用户信息、种子信息分别进行显示特征交叉。DCDN网络的两个Cross Network及Deep Network之间相互独立,Cross Netword用来获取特征之间的交叉信息,Deep Network用来对高阶非线性特征进行建模,使用者可以根据预测需求分别对网络参数进行更改。其中SR-Cross用来保证种子新闻与推荐新闻之间的相关性,UR-Cross结合用户画像提高用户的阅读兴趣。在两个真实数据集上的实验证明,DCDN算法在保证速度的同时,对比其他深度学习模型有着更好的准确性表现,具有工程实用性。
he recommendation system is widely used in Internet products, and the recommendation algorithm is paid more and more attention by researchers. This paper proposes Double Cross & Deep Network (DCDN) algorithm for news recommendation. On the basis of DCN network, this algorithm proposes a new double-crossing depth network, which extracts the features of "related news" in the recommended candidate set separately, and displays the feature crossing with the user information and the seed news information respectively. The two Cross networks and Deep networks of DCDN are independent from each other. Cross Netword is used to obtain the Cross information between features, and Deep Network is used to model high-order nonlinear features. Users can change Network parameters according to the prediction requirements. Among them, SR-Cross is used to ensure the correlation between seed news and recommended news, and UR-Cross combines with user portrait to improve users\' reading interest. The experiment on two real data sets proves that the DCDN algorithm has better accuracy performance compared with other deep learning models and is practical in engineering while guaranteeing the speed.
王玉龙、杨智鸿
计算技术、计算机技术
深度学习神经网络特征交叉新闻推荐
eep Learning Neural Networks Feature Crossing News Recommendation
王玉龙,杨智鸿.基于双交叉深度网络的新闻推荐算法[EB/OL].(2020-03-25)[2025-08-23].http://www.paper.edu.cn/releasepaper/content/202003-279.点此复制
评论