  Predicting user positive response (e.g., purchases and clicks) probability is
a critical task in Web applications. To identify predictive features from raw
data, the state-of-the-art extreme deep factorization machine (xDeepFM) model
introduces a compressed interaction network (CIN) to leverage feature
interactions at the vector-wise level explicitly. However, since each hidden
layer in CIN is a collection of feature maps, it can be viewed essentially as
an ensemble of different feature maps. In this case, only using a single
objective to minimize the prediction loss may lead to overfitting. In this
paper, an ensemble diversity enhanced extreme deep factorization machine model
(DexDeepFM) is proposed, which introduces the ensemble diversity measure in CIN
and considers both ensemble diversity and prediction accuracy in the objective
function. In addition, the attention mechanism is introduced to discriminate
the importance of ensemble diversity measures with different feature
interaction orders. Extensive experiments on two public real-world datasets
show the superiority of the proposed model.
