We examined the use of the Ising model as an L0 regularization method for
field-aware factorization machines (FFM). This approach improves generalization
performance and has the advantage of simultaneously determining the best
feature combinations for each of several groups. We can deepen the
interpretation and understanding of the model from the similarities and
differences in the features selected in each group.

使用 Ising 模型作为 L0 正则化方法提高场感知分解机 (FFM) 的泛化性能，同时确定每个组的最佳特征组合，从每个组选择的特征的相似性和差异性进一步解释和理解模型。

通过 Ising 模型对领域感知因子分解机进行 L0 正则化

L0 Regularization of Field-Aware Factorization Machine through Ising  Model

Predicting user response is one of the core machine learning tasks in
computational advertising. Field-aware Factorization Machines (FFM) have
recently been established as a state-of-the-art method for that problem and in
particular won two Kaggle challenges. This paper presents some results from
implementing this method in a production system that predicts click-through and
conversion rates for display advertising and shows that this method it is not
only effective to win challenges but is also valuable in a real-world
prediction system. We also discuss some specific challenges and solutions to
reduce the training time, namely the use of an innovative seeding algorithm and
a distributed learning mechanism.

介绍了一种预测显示广告中用户点击率和转化率的实时预测系统，该系统采用基于领域的分解机方法，得到了良好的预测效果。我们还讨论了模型训练中的一些挑战和解决方案。