ABSTRACT
Hybrid recommendation algorithms perform well in improving the accuracy of recommendation systems. However, in specific applications, they still cannot reach the requirements of the recommendation target due to the gap between the design of the algorithms and data characteristics. In this paper, in order to learn higher-order feature interactions more efficiently and to distinguish the importance of different feature interactions better on the prediction results of recommendation algorithms, we propose a light and FM deep neural network (LFDNN), a hybrid recommendation model including four modules. The LightGBM module applies gradient boosting decision trees for feature processing, which improves LFDNN's ability to handle dense numerical features; the shallow model introduces the FM model for explicitly modeling the finite-order feature crosses, which strengthens the expressive ability of the model; the deep neural network module uses a fully connected feedforward neural network to allow the model to obtain more high-order feature crosses information and mine more data patterns in the features; finally, the Fusion module allows the shallow model and the deep model to obtain a better fusion effect. The results of comparison, parameter influence and ablation experiments on two real advertisement datasets shows that the LFDNN reaches better performance than the representative recommendation models.
