Factorization machine (FM) is a prevalent approach to modeling pairwise
(second-order) feature interactions when dealing with high-dimensional
sparse data. However, on the one hand, FM fails to capture higher-
order feature interactions suffering from combinatorial expansion. On
the other hand, taking into account interactions between every pair
of features may introduce noise and degrade prediction accuracy. To
solve the problems, we propose a novel approach, Graph Factoriza-
tion Machine (GraphFM), by naturally representing features in the
graph structure. In particular, we design a mechanism to select the
beneficial feature interactions and formulate them as edges between
features. Then the proposed model, which integrates the interaction
function of FM into the feature aggregation strategy of Graph Neu-
ral Network (GNN), can model arbitrary-order feature interactions
on the graph-structured features by stacking layers. Experimental
results on several real-world datasets have demonstrated the ratio-
nality and effectiveness of our proposed approach. The code and
data are available at https://github.com/CRIPAC-DIG/GraphCTR.