| |
| The 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. |
|
| Keywords:Deep Learning, Neural Networks, Feature Crossing, News Recommendation |
|
