  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 model (xDeepFM)
introduces a new interaction network to leverage feature interactions at the
vector-wise level explicitly. However, since each hidden layer in the
interaction network 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 and
generate correlated errors. In this paper, an ensemble diversity enhanced
extreme deep factorization machine model (DexDeepFM) is proposed, which designs
the ensemble diversity measure in each hidden layer 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 three public real-world datasets are conducted to show the
effectiveness of the proposed model.
