Sparse Prediction Based On Feature Implicit Relationship

Sparse data is the data with most values missing or zero in the dataset,how to mine the implicit relationship between sparse data features and predictive analysis of sparse data is the main research issue of this paper.At present,the mainstream methods of mining implicit relationship between features cannot extract the implicit relationship between features for deeper learning.By enriching and expanding the implicit relationship between features in the feature learning process,the learning ability of the implicit relationship between features is improved.This paper proposes a sparse prediction method based on the implicit relationship between features,and combines rich feature information to participate in the automatic learning of implicit relationships between features.Firstly,this paper proposes a sparse prediction model framework FIRM based on the implicit relationship between features,which integrates multi-channel feature information into the learning of implicit relationships between features.Secondly,aiming at the transitibility and reusability of features,this paper proposes an InteractionNN model based on shortcut connection and layer loss.This model uses the transitivity of features to construct low-order feature interactions between features,and learns high-order feature interactions between features by utilizing the reusability of features.Finally,for the low-level implicit relationship between features,this paper proposes a MINN model based on hybrid low-order feature cross extraction,which introduces third-order feature interactions when learning low-order feature interactions,enriching the diversity of low-order features interaction.In this paper,a large number of comparative experiments are carried out on the proposed dataset.The experimental results show that the proposed model can better mine the implicit relationship between features.InteractionNN model and MINN model can automatically learn the more implicit relationships between features and achieve better performance.The method proposed in this paper can meet the requirements at the practical application level,and can automatically learn the implicit relationship between features automatically,while avoiding the waste of manpower.