Most Beneficial Feature Graph

In the future, we will explore adaptive learning of the most beneficial number of feature interactions for each layer and more advanced ways to model feature interaction graphs. Funding Statement This research was funded by National Key Research and Development Program of China under grant number 2018YFC1503204, and National Natural Science

the number of detected edges in feature graphs for benecial feature interaction detection. 3 Problem Formulation and Denitions Consider a dataset with input-output pairs D fX ny ng 1 n N, where y n2RZ, X n fc k x kg k2J n is a set of categorical features c with their values x, J n Jand Jis an index set of all features in D

Graph Neural Networks GNNs have demonstrated strong capabilities in processing structured data. While traditional GNNs typically treat each feature dimension equally important during graph convolution, we raise an important question Is the graph convolution operation equally beneficial for each feature? If not, the convolution operation on certain feature dimensions can possibly lead to

Existing visualizations mainly focusing on feature importance. This paper proposes a FIFI graph, a novel visualization for interpreting ML models. The intuitive idea is to plot the relative importance of features and relative strength of interactions between features into a single plot. The proposed visualization has been modelled as a network

To make the best out of feature interactions, we propose a graph neural network approach to effectively model them, together with a novel technique to automatically detect those feature interactions that are beneficial in terms of recommendation accuracy. The automatic feature interaction detection is achieved via edge prediction with an L0

In this paper, we present a novel dual feature graph enhanced model, namely FinalGNN, which integrates feature interaction selection and field-aware interaction aggregation within an innovative interaction layer of the GNN architecture.This innovative interaction layer is designed to significantly diminish high-order noisy interactions while identifying beneficial feature interactions as

ture interactions to find the most beneficial feature interactions 25, 33. This leaves the complexity issue unsolved and they resort to detecting only limited order e.g., combinations of up to four features beneficial feature interactions Figure 1left. Hence, it is still an urgent yet challenging problem to efficiently detect arbitrary

Feature selection in Knowledge Graphs KGs is increasingly utilized in diverse domains, including biomedical research, Natural Language Processing NLP, and personalized recommendation systems. Streamlined ML models require fewer computational resources, and are beneficial in resource-constrained scenarios like edge computing 15, 16

Detecting beneficial feature interactions is essential for recommender systems to achieve accurate recommendation prediction. Shu Wu, Xiaoyu Zhang, and Liang Wang. 2019. Fi-GNN Modeling Feature Interactions via Graph Neural Networks for CTR Prediction. In CIKM. 539--548. Google Scholar 22 Zekun Li, Shu Wu, Zeyu Cui, and Xiaoyu Zhang

the number of detected edges in feature graphs for benecial feature interaction detection. Problem Formulation and Denitions Consider a dataset with input-output pairs D fX ny ng 1 n N, where y n2RZ, X n fc k x kg k2J n is a set of categorical features c with their values x, J n Jand Jis an index set of all features in D. For ex-