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

Structured data in the form of tabular datasets contain features that are
distinct and discrete, with varying individual and relative importances to the
target. Combinations of one or more features may be more predictive and
meaningful than simple individual feature contributions. R's mixed effect
linear models library allows users to provide such interactive feature
combinations in the model design. However, given many features and possible
interactions to select from, model selection becomes an exponentially difficult
task. We aim to automate the model selection process for predictions on tabular
datasets incorporating feature interactions while keeping computational costs
small. The framework includes two distinct approaches for feature selection: a
Priority-based Random Grid Search and a Greedy Search method. The
Priority-based approach efficiently explores feature combinations using prior
probabilities to guide the search. The Greedy method builds the solution
iteratively by adding or removing features based on their impact. Experiments
on synthetic demonstrate the ability to effectively capture predictive feature
combinations.

该研究论文提出了两种自动化模型选择方法，分别是基于优先级的随机网格搜索和贪婪搜索方法，应用于包含特征交互的表格数据集，有效捕获预测性特征组合。